Multi-phoneme streamer and knowledge representation speech recognition system and method

ABSTRACT

A system and method related to a new approach to speech recognition that reacts to concepts conveyed through speech. In its fullest implementation, the system and method shifts the balance of power in speech recognition from straight sound recognition and statistical models to a more powerful and complete approach determining and addressing conveyed concepts. This is done by using a probabilistically unbiased multi-phoneme recognition process, followed by a phoneme stream analysis process that builds the list of candidate words derived from recognized phonemes, followed by a permutation analysis process that produces sequences of candidate words with high potential of being syntactically valid, and finally, by processing targeted syntactic sequences in a conceptual analysis process to generate the utterance&#39;s conceptual representation that can be used to produce an adequate response. The invention can be employed for a myriad of applications, such as improving accuracy or automatically generating punctuation for transcription and dictation, word or concept spotting in audio streams, concept spotting in electronic text, customer support, call routing and other command/response scenarios.

FIELD OF THE INVENTION

The present invention relates generally to speech processing. Morespecifically, the invention relates to speech processing used by humansand interpreted by machines where speech content is restricted only byconcepts conveyed instead of syntactic related constraints.

BACKGROUND OF THE INVENTION

Speech recognition is defined as the process allowing humans to interactwith machines by using speech. Scientists have worked for years todevelop the capability for machines to understand human speech. Theapplications of this capability are obvious. People can interface withmachines through speech, as opposed to the cryptic command inputs thatare the norm with today's personal computers, telephony devices,embedded devices and other programmable machinery. For example, a personwho wants to access information from a telephone may need to listen tomultiple prompts and navigate through a complex phone system by pressingkeys on a keypad or matching predefined keywords to get adequateinformation retrieved. This time-consuming process frustrates, and evensometimes discourages the user, and increases the cost for theinformation provider.

The most common approach to speech recognition relates to sound analysisof a digitized audio sample, and the matching of that sound sample tostored acoustic profiles representative of pre-defined words orutterances. Techniques for such matching include the Hidden Markov Model(HMM) and the Backus-Naur (BNF) techniques, both well known in the art.Typically, current techniques analyze audio streams and identify onesingle most probable phoneme per time-slice, while introducing aprobabilistic bias for the following time-slice to recognize a singlemost probable phoneme. A successful “match” of an audio sample to anacoustic profile results in a predefined operation to be executed. Suchtechniques typically force users to adapt their behavior by limitingtheir vocabulary, forcing them to learn commands that are recognized bythe system or having them react to prompts taking significant timebefore the information of interest to them is communicated.

One of the greatest obstacles to overcome in continuous speechrecognition is the ability to recognize words when uttered by personshaving different accents and/or voice intonations. For example, manyspeech recognition applications cannot recognize spoken words that donot match the stored acoustic information due to particularpronunciation of that word by the speaker. Often users of speechrecognition programs must “train” their own speech recognition system byreading sentences or other materials to permit the machine to recognizethat user's pronunciation of words. Such an approach cannot be used,however, for the casual user of a speech recognition system, sincespending time to train the system would not be acceptable.

Several approaches involve the use of acoustical models of various wordsto identify words in digitized audio data. For example, U.S. Pat. No.5,033,087 issued to Bahl et. al. and titled “Method and Apparatus forthe Automatic Determination of Phonological Rules as For a ContinuousSpeech Recognition System,” the disclosure of which is herebyincorporated by reference in a manner consistent with this disclosure,discloses the use of acoustical models of separate words in isolation ina vocabulary. The system also employs phonological rules which model theeffects of coarticulation to adequately modify the pronunciations ofwords based on previous words uttered.

Similarly, U.S. Pat. No. 5,799,276 issued to Komissarchik et. al. andtitled “Knowledge-Based Speech Recognition System and Methods HavingFrame Length Computed Based Upon Estimated Pitch Period of VocalicIntervals,” the disclosure of which is hereby incorporated by referencein a manner consistent with this disclosure, discloses an apparatus andmethod for translating an input speech signal to text. The apparatussegments an input speech signal based on the detection of pitch periodand generates a series of hypothetical acoustic feature vectors thatcharacterize the signal in terms of primary acoustic events, detectablevowel sounds and other acoustic features. The apparatus and methodemploy a largely speaker-independent dictionary based upon theapplication of phonological and phonetic/acoustic rules to generateacoustic event transcriptions. Word choices are selected by comparingthe generated acoustic event transcriptions to the series ofhypothesized acoustic feature vectors.

Another approach is disclosed in U.S. Pat. No. 5,329,608 issued toBocchieri et. al. and titled “Automatic Speech Recognizer,” thedisclosure of which is hereby incorporated by reference in a mannerconsistent with this disclosure. Bocchieri discloses an apparatus andmethod for generating a string of phonetic transcription strings fromdata entered into the system and recording that in the system. A modelis constructed of sub-words characteristic of spoken data and comparedto the stored phonetic transcription strings to recognize the spokendata.

Yet another approach is to select candidate words by slicing a speechsection by the unit of a word by spotting and simultaneously matching bythe unit of a phoneme, as disclosed in U.S. Pat. No. 6,236,964 issued toTamura et. al. and titled “Speech Recognition Apparatus and Method forMatching Inputted Speech and a Word Generated From Stored ReferencePhoneme Data,” the disclosure of which is hereby incorporated byreference in a manner consistent with this disclosure.

As previously noted, several approaches use Hidden Markov Modeltechniques to identify a likely sequence of words that could haveproduced a given speech signal. For example, U.S. Pat. No. 5,752,227issued to Lyberg and titled “Method and Arrangement for Speech to TextConversion,” the disclosure of which is hereby incorporated by referencein a manner consistent with this disclosure, discloses identification ofa string of phonemes from a given input speech by the use of HiddenMarkov Model techniques. The phonemes are identified and joined togetherto form words and phrases/sentences, which are checked syntactically.

Typically, in prior art approaches, too much emphasis is put on straightsound recognition instead of recognizing speech as a whole, where syntaxis used exclusively to build a concept and the concept itself is used inorder to produce an adequate response.

SUMMARY OF THE INVENTION

The system and method of this invention provides a natural languagespeech recognition process allowing a machine to recognize human speech,conceptually analyze that speech so that the machine can “understand” itand provide an adequate response. The approach of this invention doesnot rely on word spotting, context-free grammars or other single-phonemebased techniques to “recognize” digitized audio signals representativeof the speech input and consequently does not probabilistically bias thepattern recognition algorithm applied to compare stored phonemesprofiles in each cluster with the audio data. Instead the approach ofthis invention is to recognize multiple, sometimes alternative, phonemesin the digitized audio signals; build words through streaming analysis,syntactically validate sequences of words through syntactic analysis,and finally, analyze selected syntactically valid sequences of wordsthrough conceptual analysis. The invention may utilize some methodsrelated to artificial intelligence and, more specifically, recurrentneural networks and conceptual dependency to achieve these objectives.By conceptually analyzing the speech input, the machine can “understand”and respond adequately to that input. In addition, the invention isapplicable to speakers of different accents and intonations by usingclusters.

More specially, the invention relates to a multi-phoneme streamer andknowledge representation system and method. By combining novel methodsof phoneme recognition based on multi-phoneme streaming, and applyingconceptual dependency principles to most probable recognizedsyntactically valid sequences of candidate words obtained from thepermutation of all recognized phonemes in their respective time-slice ofan audio sample, the invention enables humans to communicate withmachines through speech with little constraint in regards to syntax ofcommands that can be recognized successfully. Although most of thecontent of this disclosure relates to an English implementation of theinvention, this approach can be used for any language.

The invention utilizes clusters as a grouping of all phoneme speechrelated data in a targeted group of individuals. (Every language isbased on finite set of phonemes, such as about 45 phonemes for English.A cluster is a set of reference phonemes [e.g., 45 phonemes for English]for a particular speaker type, such as a man/woman, adult/child, region,or any combination thereof.) Preferably, the computerized system andmethod evaluates all probabilities without bias of all phonemes in allclusters for an audio data input through the use of a patternrecognition algorithm. A list of candidate words is then built, whilekeeping the starting time in the audio input for each of them, using allphonemes identified from a unique cluster in the audio data as exceedinga minimal probability set by the pattern recognition algorithm. Using adictionary that associates pronunciations to spellings and syntacticparts of speech, a syntactic analysis process builds all syntacticallyvalid sequences of words from all possible permutations of candidatewords in the words list while respecting pronunciation of wordsboundaries. Only high potential of being correctly formed syntacticsequences, for example sentences or other syntactic organizations, arelater analyzed conceptually. These sequences preferably encapsulate theentire audio data (i.e., all recognized phonemes) although the inventionis operative on any given syntactic organization according to theprogramming engineer's specifications. A subset of English syntacticorganization rules that are applicable to the invention are discussed inJurafsky, Daniel and Martin, James H., Speech and language processing,Prentice Hall, New Jersey, 2000, pages 332-353, the disclosures of whichare herein incorporated by reference in a manner consistent with thisdisclosure.

Conceptual analysis is performed through predicate calculus operationsthat are driven by Predicate Builder scripts associated with each wordand part of speech. Conceptual analysis initially involves searching foran object of knowledge in the syntactic hierarchy derived from thesyntactic organization (i.e., what is being talked about, a person, aflight, etc), by parsing all noun phrases, as an example for the Englishlanguage, and detecting a resulting valid Predicate structure. Once anobject of knowledge is successfully detected, the entire syntacticorganization is parsed, and the Predicate structure resulting fromconceptual analysis is interpreted in order to produce an adequateanswer. If an answer cannot be produced from conceptual analysis of thesyntactic organization's hierarchy, other syntactic organizationshierarchies that encapsulate the entire, or any desired portion, of theaudio data are analyzed conceptually following the same process until atleast one succeeds; although the successful conceptual representationmay contain some kind of inquiry anomaly derived from the syntacticorganization's conceptual analysis, consequently signaling the desiredcontinuation of conceptual analysis processing to eventually build aconceptual representation which contains preferable inquiry anomalyidentified in it.

One advantage of the system and method of the invention is that it doesnot require a predefined syntax from the speaker to be observed in orderfor a command to be recognized successfully. Another advantage is thatsystems implementing this method do not require a sound input with highsampling rate in order to be analyzed successfully; for example,telephony systems can function more efficiently with this method thanprior art approaches. This indeed significantly improves the balance ofpower in speech recognition by inserting a process where conceptsconveyed have some weight in the recognition task; in contradiction toprior art approaches where emphasis is put on straight soundrecognition.

The system includes an audio input device, an audio input digitizer, aunit for recognizing phonemes by applying pattern recognitionalgorithms, a phoneme stream analyzer for building a list of probablewords based on the probable phonemes by reference to a dictionarystructure, a syntactic analyzer for building syntactically validsequences of words from the list of probable words, a conceptualanalyzer for building conceptual representations of syntactically validsequences, and a post analysis process that builds conceptualrepresentations of adequate responses to the original inquiry.

Some of the techniques are based on the concept of Conceptual Dependency(CD), as first set forth by Schank. Many references are available thatexplain in depth the approach of Schank, which on a very broad level isto remove syntax from a statement leaving the concept intact. In thatway, statements of differing syntax yet similar concept are equalized.Such references include Schank, Roger C. and Colby, Kenneth M., Computermodels of thought and language, W.H. Freeman and Company, San Francisco,1973, pages 187-247; Riesbeck, Christopher K. and Schank, Roger C.,Inside case-based reasoning, Lawrence Erlbaum associates publishers, NewJersey, 1989; and Riesbeck, Christopher K. and Schank, Roger C., Insidecomputer understanding, Lawrence Erlbaum associates publishers, NewJersey, 1981. The disclosures of each of these references areincorporated by reference herein in a manner consistent with thisdisclosure.

It is an object of the invention to:

-   -   i. provide a method for speech recognition that builds words and        syntactically valid sequences of words from the phonemes        contained in a digitized audio data sample.    -   ii. provide a method that combines artificial intelligence and        recurrent neural networks with phoneme recognition and        Conceptual Dependency that allows a machine to conceptually        “understand” a digitized audio data sample.    -   iii. provide a method of conceptual speech recognition that        allows a machine to formulate an adequate response to a        digitized audio data sample based on the machine's conceptual        “understanding” of the input.    -   iv. provide a method of conceptual speech recognition that is        speaker independent.    -   v. provide a method of conceptual speech recognition that        recognizes not only words but concepts in a digitized audio        sample.    -   vi. provide a method of conceptual speech recognition that        recognizes concepts in a digitized audio sample substantially        regardless of the speaker's vocal intonation and/or accent.    -   vii. provide a system utilizing a method of conceptual speech        recognition that can be accessed and used by numerous users        without prior training and/or enrollment by those users in the        system.    -   viii. provide a system and method for word spotting in an audio        stream.    -   ix. provide a system and method for concept spotting in an audio        stream or electronic text.    -   x. provide a system and method for validating punctuation and        syntactic relationships in dictation speech recognition.    -   xi. provide a system and method that can generate punctuation in        existing dictation systems so punctuation marks do not have to        be read into dictation, allowing the user to speak more        naturally.    -   xii. provide a system and method that can enhance recognition        accuracy of existing dictation systems.

These and other aspects of the invention will become clear to those ofordinary skill in the art based on the disclosure contained herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be described with reference to the followingdrawings, in which like elements are referenced with like numerals.

FIG. 1 is a schematic of one embodiment of the method of the invention.

FIG. 2 is a schematic of one embodiment of the system of the invention.

FIG. 3 is a flow diagram of the Phoneme Recognition process according toone embodiment of the invention.

FIG. 4 is a flow diagram of the Phoneme Stream Analysis processaccording to one embodiment of the invention.

FIG. 5 is a flow diagram of the Process Search Paths sub-processaccording to one embodiment of the invention.

FIG. 6 is a schematic of the Phoneme Stream Analysis structures and flowdiagrams of the Get Stream Length sub-process and the Promote Pathsub-process according to one embodiment of the invention.

FIG. 7 is a flow diagram of the Flatten Scripts sub-process according toone embodiment of the invention.

FIG. 8 is a schematic of the Dictionary structures and a flow diagram ofthe Dictionary Forward sub-process according to one embodiment of theinvention.

FIG. 9A is a schematic of an exemplary syntactic transform scriptaccording to one embodiment of the invention.

FIG. 9B is a schematic of an exemplary number transform script accordingto one embodiment of the invention.

FIG. 9C is a schematic of an exemplary time transform script accordingto one embodiment of the invention.

FIG. 9D is a schematic of an exemplary custom transform script accordingto one embodiment of the invention.

FIG. 10 is a flow diagram of the Process Script Files sub-process andthe Syntactic Analysis process according to one embodiment of theinvention.

FIG. 11 is a flow diagram of the Link Sequences Stream sub-processaccording to one embodiment of the invention.

FIG. 12 is a flow diagram of the Test Stream sub-process according toone embodiment of the invention.

FIG. 13 is a flow diagram of the Time Producer Permutation Callbacksub-process and the Permutation Callback sub-process according to oneembodiment of the invention.

FIG. 14 is a flow diagram of the Number Producer Permutation Callbacksub-process according to one embodiment of the invention.

FIG. 15 is a flow diagram of the Process Script Line sub-process and theLoad Script File sub-process according to one embodiment of theinvention.

FIG. 16 is a schematic of transform script file structures and also is aflow diagram of the Get Condition Entry sub-process and the FinalizeScript Line sub-process according to one embodiment of the invention.

FIG. 17 is a flow diagram of the Conceptual Analysis and Post Analysisprocesses according to one embodiment of the invention.

FIG. 18 is a flow diagram of the Calculate Predicate for Stream andCalculate Predicate for Children sub-processes according to oneembodiment of the invention.

FIG. 19 is a flow diagram of the Calculate Predicate for NOUN_PHRASEStream sub-process according to one embodiment of the invention.

FIG. 20 is a flow diagram of the Calculate Predicate for SENTENCE Streamsub-process according to one embodiment of the invention.

FIG. 21 is a flow diagram of the Find Packet sub-process according toone embodiment of the invention.

FIG. 22 is a flow diagram of the Evaluate Packet and Drill for Packetsub-process according to one embodiment of the invention.

FIG. 23 is a flow diagram of the Set Transient Information sub-processand a Syntactic Hierarchy example according to one embodiment of theinvention.

DETAILED DESCRIPTION OF THE INVENTION

The system and method of the invention is designed to operate on anyprogrammable device available now or hereafter developed, includingpersonal computers and networks, embedded devices, main frames,distributed networks or other means of computing that may evolve overtime. The computer should be capable of sufficient speed and containsufficient memory in order to operate the various subroutines andprocesses, described below, of the conceptual speech recognition method.The invention may be used on a personal computer or embedded device byway of audio input, which may be accomplished by numerous acceptablemethods now known or later developed. The only requirement of the audiodata input is that it be digitized either prior to being input, orotherwise after being input into a system operating using the invention.The audio data input could be digitized according to well understooddigitization techniques, including, for example, PCM (Pulse CodeModulation), DM (Delta Modulation), APCM (Adaptive Pulse CodeModulation), ADPCM (Adaptive Delta PCM) and LPC (Linear PredictiveCoding); although other methods of digitizing the audio data input couldbe utilized and the foregoing references are not intended to belimiting. Standard references well known to those skilled in the artteach various techniques for digitizing speech signals. See for exampleDigital Processing of Speech Signals by L. R. Rabiner and R. W. Schafer(Prentice-Hall 1978), and Jurafsky, Daniel and Martin, James H., Speechand language processing, Prentice Hall, New Jersey, 2000, thedisclosures of which are hereby incorporated by reference in a mannerconsistent with this disclosure.

It should be understood by those of skill in the art that digitizing thespeech can occur in multiple fashions by multiple devices at multiplelocations. For example, the speech can be digital encoded in the variousfashions discussed previously (PCM, ADPCM, etc.). The speech can bedigitized by various devices, such as a conventional A-to-D converter, acell phone, a personal computer, an embedded device, a PDA, and soforth. The speech can be digitized at various locations, such as at acell phone, PC, PDA or the like proximate to the speaker. The speech canbe digitized at a network server or other computer or embedded deviceremote from the speaker, such as at a customer service centerimplementing the present invention to field customer service calls.Finally, it should also be understood that the term “digitizing” or“digitization” should be understood to not only encompass digitallyencoding an analog signal, but also re-digitizing a presently digitalsignal. For example, if the speaker is transmitting speech through adigital cell phone as understood in the art, two phases of digitizingmay occur: one at the cell phone where the speaker's analog voice signalis converted to a digital representation for transmission over-the-air,and a second one at a speech processing system where the digital signalmay be re-digitized to provide a digital signal with the proper bitresolution, quantization, bit rate, and so forth, in accordance with therequirements of the system.

Audio input devices used by this system and method include microphone,telephone, wireless transceiver, modem, a voice recorder (analog ordigital) and any other existing or future technology permitting spokenwords to be received and converted into an electrical, electromagneticor any other physical representation. If the system is utilized on anetwork (e.g., the Internet, LAN, PAN, WAN, cellular network, PublicSwitched Telephone Network [PSTN], or the like), the network should havean interface capable of receiving the audio input. Common interfacesinclude interfaces with the PSTN where the audio input is by telephone(or with a cellular network via a wireless transceiver); a networkserver where the audio input is by Internet; In addition, the systemshould include a method for outputting the result in response to theaudio input. Such output may include digitized artificial speechgenerated by the computer through its speakers or by telephone (orwireless transceiver); a text output for medias such as the Internet (orany other similar distributed networks) and email; or any other processwhich may be invoked as a result to a successful recognition.

It is to be understood that a voice recorder encompasses analog ordigital technology for storing a representation of speech, such asanalog tape (e.g., VHS, etc.), digital tape, memories storing digitalsound files (e.g., .wav, .voc, .mp3, and the like), and so forth.Further, the interface or link between a sound source (whether it be alive source such as a person speaking into a microphone or a recordedsource such as a .wav file) and the speech processing system of thepresent invention may encompass a packet-switched network connection(e.g., Internet, WAN, PAN, LAN, etc.), a circuit-based orpacket-switched telephony connection (e.g., PSTN or cellular network), amicrowave connection, satellite connection, cable connection,terrestrial broadcast-type connection and the like. Of course, it isreadily appreciated that the interface between the sound source and thespeech processing system may be a direct connection where the soundsource and the speech processing system are essentially collocated.

Typically, the invention is accessed in a real-time interactiveenvironment between a personal computer, network server or embeddeddevice that operates the system and the persons or entities that inputthe audio data. In many situations, a business will operate the speechrecognition system of the invention through a network server to providesome information to its customers. Such information may include, forexample, bank account information, assistance with products, customerassistance with billing inquiries, driving directions, stock prices orairline flight information. These examples of the types of informationthat may be provided as a result of the conceptual speech recognitionsystem and method are exemplary only and are not intended to limit theinvention. In fact, any information may be provided in response to audioinput by use of the conceptual speech recognition system and method ofthe present invention.

Typically, a customer of a business that is utilizing the conceptualspeech recognition system and method will make some inquiry. Thevocalization of that inquiry comprises the audio data input into thesystem. Preferably, the system will provide the customer with the answerto the inquiry in real time. However, the system can also be operated ona batch basis in which case the customer may input the inquiry, and thesystem may provide the answer to the customer at a later time.

The data processor preferably will have one or more memory units forstoring received audio data input samples, and preferably maintains afile system whereby each stored audio input sample is designated withfile reference indicia. When the system has completed the speechrecognition process, the result can be referenced using the same filereference indicia such that the result of the speech recognition processcan be returned to the customer that input the data. The audio returnmay be made in real-time, or it may be returned at a later time. Thereturn may be made by any form of wired or wireless communicationincluding telephone or email or may be stored to persistent memory forlater referral.

The invention entails other applications as well. The invention may beused to provide word recognition services given an audio input.Similarly, the invention may be used to provide syntactic validation ofdictation that may be input using other phoneme recognition methods,such as HMM, to improve accuracy.

Referring to the figures, FIG. 1 depicts a flow scheme representing theinvention's overview of data flow and processes in the preferredembodiment of the invention.

In Box 102, an utterance is produced by a speaker. The speaker may be aperson or a machine. Audio may be captured by a microphone connected toa computer, a telephone line receiving a signal, an interface though anembedded device or any other means of communication that may be knowntoday or later developed. In Step 104, digital signal processing (DSP)is performed to digitize the audio signal. Virtually any DSP processknown to those skilled in the art can be used. Box 106 shows the resultof Step 104 as audio data that contains the speech information of theutterance. Step 110 uses the audio data containing speech in Box 106 andvoice models that are predefined and programmed into the system as inputto execute a Phoneme Recognition process shown in Box 108 (an exemplaryphoneme recognition process is further described in FIG. 3), which willproduce a phoneme stream shown in Box 112 (also explained in FIG. 3).The Phoneme Recognition process at Step 110 detects probable phonemesover a predefined probabilistic threshold per time-slice.

The Phoneme Recognition process is capable of detecting a plurality ofcandidate phonemes, some of which are alternative candidate phonemes,meaning that they represent different possible phonemes detected in thesame sample of speech, or audio input. It should also be noted that thethreshold employed is preferably fixed, although it could be adaptive.For example, the threshold might be automatically adjusted based onthroughput considerations. Similarly, the threshold may vary with timefor different phonemes within a given cluster, within a given cluster,or between different clusters. The threshold may also be constant andthe same for all clusters, constant but potentially different for allclusters or constant but potentially different for all phonemes within agiven cluster.

In Step 116, the Phoneme Stream Analysis process uses the phoneme streamshown in Box 112 and the Dictionary shown in Box 114 as an input. ThePhoneme Stream Analysis process at Step 116 will generate a list ofwords potentially uttered by the speaker (candidate words) ordered bytheir respective starting phoneme index in the phoneme stream, as shownin Box 118. Preferably, the Phoneme Stream Analysis process is based onpermuting all combinations of the candidate phonemes to generate aninitial list of candidate words. Candidate words may be processedaccording to a dictionary, described further below, to identify a subsetreferred to as candidate words.

In Step 122, a Syntactic Analysis process (an example of which isexplained in FIG. 10) is performed by applying transform scripts (e.g.,like the exemplary ones shown in FIG. 9) from Box 120 to the list ofwords potentially uttered from Box 118. The Syntactic Analysis processin Step 122 populates the list of potentially spoken words withsyntactic organizations in Box 124 while respecting word boundaries andrules described in transform scripts. The transform scripts may beadapted and customized for individual operations, and customization mayimprove system operation.

In Step 126, the Conceptual Analysis process uses the list of candidatewords with syntactic organizations from Box 124 as input to calculate aPredicate structure describing conceptually the inquiry uttered by thespeaker. A Predicate structure, further explained in FIG. 17, is aconceptual representation where all elements of syntax are removed; asan example, the Predicate structure of “What time is it?” would be thesame as the one of “What is the time?” since both sentences convey thesame concept and they only differ by their syntax used. The technique ofConceptual Dependency forms the basis of this aspect of the invention,which technique was first formulated by Schank. The Conceptual Analysisprocess at Step 126 generates a Predicate structure describing theinquiry in Box 128.

In Step 130, the Post Analysis process, further explained in FIG. 17,uses the inquiry Predicate structure from Box 128 in order to produce aresponse Predicate structure in Box 132. A different Predicate structureis produced to formulate a response than the one that described theinquiry since a system may well understand what is being asked, but itdoes not automatically mean it can produce an adequate response. Havingtwo separate concepts, one for the inquiry and another one for theresponse, is more adequate than using only one for the inquiry andmatching it to stored concepts that may be handled by the system,although the invention may be implemented by matching an inquiry tostored concepts if desired in a particular application.

If desired, in Step 134, the Command Handler, further explained in FIG.17, processes the response Predicate structure to produce the responsein Box 136. A Predicate structure can indeed be processed since itcontains action primitives that can be implemented in the system. As anexample, the action primitive SPEAK with the content role would generatea voice synthesizer of the filler associated with the content role,producing an audible response to the speaker. The response does not haveto be limited to an audible response, however. The response Predicatestructure may hold any operation it sees fit as a response to theinquiry (a database change, a phone connection being made, a lightturned on, or else).

Referring to FIG. 2, a flow diagram for the operation of the method ofthe invention is depicted. The first aspect of the method involvesphoneme recognition. At 200, a customer of an operator of the system'sinvention contacts the system through some communication medium andinputs an inquiry in the form of audio data which is received by thesystem. At 202, the audio input is digitized by any now known or laterdeveloped technique for digitization. At 204, the digitized audio datastream is analyzed for probable phoneme recognition, where probablephonemes for any given time-slice are detected. The probable phonemesare detected using pattern recognition algorithms known to personsskilled in the art, and may be any method now known or later developed.The pattern recognition algorithm utilizes a plurality of pre-definedclusters, seen at 206, each cluster containing a pre-defined set ofphonemes with different vocal accents and/or intonations, such as U.S.Western male, U.S. Northeastern female, etc. The pattern recognitionalgorithm determines the presence of any phonemes in the audio datasample that exceed a minimum pre-determined probability of being presentin the audio data sample. The result of the phoneme recognition processis a phoneme stream 208 which comprises a string of charactersindicative of recognized phonemes whose probability exceed the minimumprobabilistic threshold, and also includes other characters to indicatethe start time and end time of the occurrence of that phoneme in theaudio data sample as well as the positioning of data within the phonemestream.

After the probable recognized phonemes have been analyzed and thephoneme stream 208 has been generated, the phoneme stream is analyzed todetermine candidate words from the phoneme stream 208. A phoneme streamanalyzer 210 builds a list of candidate words 212. The phoneme streamanalyzer 210 refers to a pre-built dictionary 214 for informationrelated to words that may be placed in the word list 212, including suchinformation as spellings and parts of speech.

Next, the system builds a list of probable sequences of candidate wordsthat can form syntactically correct sequences 228 using the wordssequence extractor 216. This is performed by use of syntactic rulesapplied to the candidate words 212 using information associated withthose words in the Dictionary 214. Multiple sequences may be developedusing permutation analysis 218, by applying syntactic rules, ortransform scripts 222, that may be adapted for any particularapplication.

The syntactically correct syntactic organizations that use all thetime-slices from the phoneme stream, or at least those time-slicesselected by the programming engineer, are then parsed to determine theirconceptual representations 232 by the conceptual dependency parser 230.This technique, as originally formulated by Schank, is applied throughthe use of a conceptual dependency scripting language 224 and PredicateBuilder scripts 226. Once the conceptual representation of the inquiryis determined, a conceptual representation of a response is calculatedin post analysis 234. The final result is a command to execute inresponse to the inquiry.

The process of the preferred embodiment can, for ease of discussion, becategorized into four major functional processes: Phoneme Recognition,Phoneme Stream Analysis, Syntactic Analysis and Conceptual Analysis.These processes, along with the corresponding structures and examples ofdifferent scripts, are explained in detail in the following sections.

Phoneme Recognition

Turning now to FIG. 3, a flow scheme for the Phoneme Recognition processin the preferred embodiment of the invention is depicted. The result ofthe Phoneme Recognition process is a single phoneme stream of recognizedphonemes that comprises a finite sequence of N characters PS_(1−N).

The phoneme stream produced in FIG. 3 uses the following specialcharacters:

-   -   1. Opening time-slice delimiter (‘[’): identifies the beginning        of a new time-slice in the phoneme stream with at least one        recognized phoneme.    -   2. Closing time-slice delimiter (‘]’): identifies the end of the        time-slice in the phoneme stream.    -   3. Data separator (‘;’): the Data separator is used between the        Opening and Closing time-slice delimiters to separate the        starting time and ending time values as well as to separate all        data related to each phoneme recognized in a single time-slice.    -   4. Phoneme level data separator (‘,’): the Phoneme level data        separator is used to separate the phoneme from the probability        and the probability from the cluster index used to recognize the        phoneme in a time-slice of the phoneme stream (between the        Opening and Closing time-slice delimiters).

In Step 302, the process of Phoneme Recognition begins with an audiosignal as an input to the system. Methods and formats for inputtingaudio into the system are well known in the art. For example, audio maybe input into the system by reading a buffer of audio impulse generatednumeric values obtained from a microphone connected to a computer orreading any buffer in memory or on storage that represents audio impulsegenerated by any mean. Any of these known methods and formats may beused with the system.

The system has predefined clusters Cl_(1−N), each cluster Cl_(i) holdingdata required to estimate pattern equivalence for each phonemesPh_(1−N).

In Step 304, the received audio signal is split into any number ofsegments of fixed time length K. The fixed time length K in Step 304 isa constant value, preferably between 5 milliseconds and 40 milliseconds,most preferably between 10 milliseconds and 20 milliseconds. The fixedtime length for the fixed time length K is determined by experimentationand typically does not vary through a single execution of the phonemerecognition process.

In Step 306, the first time-slice K₁ taken from time zero having a fixedtime length of K is set in the system as the current time-slice K_(C).In Step 308, the first cluster Cl₁ is set as the current cluster Cl_(C).In Step 310, the first phoneme Ph₁ in the current cluster Cl_(C) is setas the current phoneme Ph_(C).

A pattern recognition algorithm is performed in Step 312 that comparesthe current phoneme Ph_(C) of the current cluster Cl_(C) to the audiodata of current time-slice K_(C). The pattern recognition algorithm maybe one of many known today to those skilled in the art. By way ofexample and not intending to limit the invention in any manner, thepattern recognition algorithm may include a recurrent neural network, atime delay neural network, a Gamma-filtered time delay neural network, astatic neural network trained with MFC coefficients, a Self-OrganizingMaps in an AKA Kohonen neural network, Formant analysis, a multivariateGaussian classifier or any other adequate pattern recognition processnow known or later developed.

In Step 314, the matching probability MP_(C) for the recognition ofPh_(C) in the audio data of K_(C) is compared to a predetermined minimalprobabilistic threshold MPT for Cl_(C). MPT is a constant valueassociated with each cluster Cl_(i) and does not change in time duringprocessing for that given cluster Cl_(i). MPT is determined byexperimentation for each cluster, and is the probabilistic valueobtained from audio test cases where correct phonemes are recognizedsuccessfully while minimizing occurrences of wrong phonemes.

If the MP_(C) for Ph_(C) is less than the minimal probabilisticthreshold MPT for the current cluster Cl_(C), the system determines ifthere are more phonemes Ph_(i) in current cluster Cl_(C) that have notbeen compared to the audio data of K_(C). If additional phonemes Ph_(i)are found in current cluster Cl_(C), in Step 318 the next phonemePh_(C+1), in current cluster Cl_(C) is set as the current phonemePh_(C).

If MP_(C) is greater or equal than MPT for the current cluster Cl_(C),the process continues at Step 320. In Step 320, the system determines ifrecognized phoneme Ph_(C) is the first phoneme with an MP_(C) thatexceeded MPT for the time-slice K_(C). If so, at Step 322, characters ofthe formula ‘Opening time-slice delimiter (‘[’) t_(SC) Data separator(‘;’) t_(FC)’ are appended to the phoneme stream where t_(SC) is thestarting time of matching phoneme Ph_(C) in the audio data measured fromthe start of the audio used as input to the process, and t_(FC) is theending time of matching phoneme Ph_(C) within the audio data measuredfrom the start of the audio used as input to the process. The phonemestream is a continuous but finite sequence of characters PS_(1−N) storedin memory in the system that represent the sequence of probable phonemesPh_(i) that were recognized over the predefined MPT to their respectivecluster Cl_(i) with their associated probability MP_(i), starting timet_(si) and ending time t_(Fi) are expressed from the elapsed times fromthe start of the audio data used as input to the process.

If a recognized phoneme Ph_(C) is not the first time a phoneme Ph_(i)with a MP_(C) that exceeded MPT for the current time-slice K_(C) wasdetected by the pattern recognition algorithm, in Step 324, charactersof the formula ‘Data separator (‘;’) Ph_(C) Phoneme level data separator(‘,’) MP_(C) Phoneme level data separator (‘,’) Cl_(C)’ are appended tothe phoneme stream.

From Step 324, the process moves to Step 318 to determine if there areadditional phonemes Ph_(i) in the current cluster Cl_(C) that have nothad MP determined for the audio data of time-slice K_(C). If there areadditional phonemes Ph_(i) in current cluster Cl_(C), Ph_(C+1), is setas Ph_(C) and the MP for each additional phoneme Ph_(i) in clusterCl_(C) is determined as described in Step 312. The process continues aspreviously described until all phonemes through Ph_(N) in currentcluster Cl_(C) have had MP determined for the audio data of time-sliceK_(C) in Step 318.

Once it is determined in Step 318 that all phonemes Ph_(1−N) of clusterCl_(C) have been compared to audio data of time-slice K_(C), in Step 326the system determines if there were any phonemes Ph_(i) for which the MPexceeded the MPT for the cluster Cl_(C). If so, in Step 328 the Closingtime-slice delimiter is appended to the phoneme stream of recognizedphonemes from cluster Cl_(C). At Step 330, the system determines if allclusters Cl_(i) have been analyzed. If not, the next cluster Cl_(C+1),is set as current cluster Cl_(C) and the process begins again at Step332 until all clusters Cl_(1−N) have been analyzed.

If in Step 326 the system determines that there were no successfulphoneme recognitions, i.e., there was no Ph_(C) in current clusterCl_(C) for which MP_(C) exceeded MPT for the audio data of time-sliceK_(C), the system determines in Step 330 if all clusters Cl_(1−N) havebeen analyzed. If there are more clusters Cl_(i), the system designatesthe next cluster Cl_(C+1) as current cluster Cl_(C) at Step 332. Theprocess of phoneme recognition begins again at Step 310 and continuesuntil all clusters Cl_(1−N) have been analyzed.

If at Step 330 the system determines that all clusters Cl_(1−N) havebeen analyzed, at Step 336 the system tests if there is additional audiodata in addition to that contained in the current time-slice K_(C). Ifso, at Step 334 the system selects audio data for a following time-sliceK_(C+1) for a time-slice of fixed time length K beginning at a time ½ ofthe fixed time length K (½K) past the beginning of current time-sliceK_(C). The system begins the Phoneme Recognition process again at Step308 where K_(C) equals K_(C+1). The system continues to analyzetime-slices in the audio signal in this manner, advancing the beginningof each time-slice K_(i) having fixed time length K from the beginningof the current time-slice K_(C) by a time between 0.4K and 0.6K, butpreferably ½K, until the entire audio signal has been analyzed. If atStep 336 there are no more time-slices after the current time-sliceK_(C) having a fixed time length K and beginning at a time that isadvanced past the beginning of current time-slice K_(C) by a time of ½K,the system notes at Step 338 the end of the Phoneme Recognition process.

The result of the Phoneme Recognition process at Step 338 is a singlephoneme stream of recognized phonemes from the audio data into thesystem, which phoneme stream comprises a finite sequence of N charactersPS_(1−N). This phoneme stream is then analyzed to build a list ofprobable words recognized in the phoneme stream.

Phoneme Stream Analysis

FIG. 4 depicts a flow scheme for the Phoneme Stream Analysis process inthe preferred embodiment of the invention. The Phoneme Stream Analysisprocess decodes the phoneme stream PS_(1−N) produced by the PhonemeRecognition process explained in FIG. 3 and produces a unique list ofwords, candidate words, ordered by their respective starting phonemeindex in the phoneme stream stored in a TRecoLst structure as seen inBox 602 and Box 604 of FIG. 6.

In order to permute candidate phonemes from every time-slice with othercandidate phonemes from the following time-slice and produce allcandidate words from such permutations, search paths are used. Eachsearch path, as seen in the TSrchPath structure definition in Box 606 inFIG. 6, holds a single phoneme permutation sequence obtained from theanalysis of contained phonemes of a phoneme stream. That phonemepermutation sequence in TSrchPath is kept in mPhonemeStream and needs tobe a sequence that refers to a partial pronunciation of at least oneword in the dictionary. As an example, a search path could contain aphoneme sequence in mPhonemeStream like the pronunciation of ‘deliv’,which is part of the pronunciation of the pronunciation of the word‘delivery’ that is contained in the dictionary. As soon as a singlephoneme is added to mPhonemeStream in a search path where the resultedmPhonemeStream is not a partial pronunciation stored in the dictionary,the search path is dropped. As an example, adding the ‘c’ phoneme to‘deliv’ would result in the search path being dropped since there are nosuch words in the dictionary that starts with the pronunciation‘delivc’. A search path is dropped by not being promoted. That is, foreach time-slice, the Phoneme Stream Analysis process works on WorkingPaths WP—which contains search paths that were obtained from the subsetof promoted search paths from previous time-slice. As new phonemes areappended to existing search paths in WP, only those that are allowedprovided that a dictionary forward is valid—signaling that a validpartial pronunciation is under construction—will be promoted. For thefollowing time-slice, only promoted search paths will be used as a basisfor WP and WP is dropped. While performing that process, if a completesequence of phonemes is detected in mPhonemeStream—a complete sequencemeaning that it is actually related to the complete pronunciation of aword instead of only a partial pronunciation—the word is added in thewords list WL. Bridging also needs to be processed during Phoneme StreamAnalysis. Bridging is related to single phonemes that are shared betweentwo words. As an example, if a speaker utters ‘that text too’, the ‘t’phoneme between ‘that’ and ‘text’ was bridged between both words (thereis only one phoneme although it was used to pronounce both words) aswell as the ‘t’ phoneme between ‘text’ and ‘too’.

FIG. 4 is tightly related to FIG. 5, FIG. 6, FIG. 7 and FIG. 8 thatdescribe sub-processes used by the Phoneme Stream Analysis process. ThePhoneme Stream Analysis process may be implemented in other ways knownto those skilled in the art. By way of example and not intending tolimit the invention in any manner, FIG. 4 describes the preferredprocess used in the invention. Any alternative process that uses aphoneme stream and produces a two-dimensional array of recognized wordsordered by their starting phoneme index is equivalent.

In Step 402, the process of Phoneme Stream Analysis begins with thephoneme stream PS_(1−N) that resulted from the Phoneme Recognitionprocess in Step 338 of FIG. 3. In Step 404, variables used in thePhoneme Stream Analysis process are cleared. Character Buffer CB is arange in memory that can hold some content to be filled later in thePhoneme Stream Analysis process. Start Time ST and End Time ET arenumbers. Phoneme PH is a single character, and must be a letter eitheruppercase or lowercase. Probability PB, Cluster Index CI and Index inStream IS are numbers. Working Paths WP and Promoted Paths PP, areTPaths structure as seen in Box 608 of FIG. 6. Bridge List BL is aTBridge structure as seen in Box 610 of FIG. 6. Words List WL is aTRecoLst structure as seen in Box 604 of FIG. 6. Time-Slices Count TSCis a number that holds the total of time-slices in the phoneme streamanalyzed. The Phoneme Stream Analysis process also uses the globalvariable Indexes IND that is a TIndex as seen in Box 808 of FIG. 8. INDis the unique dictionary structure required in order to perform alldictionary related operations.

As previously discussed, the phoneme stream that resulted in Step 338 ofFIG. 3 is a finite sequence of N characters PS_(1−N). In Step 406 of thePhoneme Stream Analysis process, the current phoneme stream characterPS_(C) is set to the first character of the phoneme stream PS₁. In Step408, PS_(C) is evaluated to test if it is a Data separator character. InStep 428, PS_(C) is evaluated to test if it is a Phoneme level dataseparator character. In Step 430, PS_(C) is evaluated to test if it isan Opening time-slice delimiter. In Step 432, PS_(C) is evaluated totest if it is the Closing time-slice delimiter.

If Step 408, Step 428, Step 430 and Step 432 all fail, then characterPS_(C) is appended to CB. In Step 462, if PS_(C) is the final characterof the phoneme stream PS_(N), the process is halted at Step 466. IfPS_(C) is not the final character PS_(N) of the phoneme stream, at Step462 the current phoneme stream character PS_(C) is set to the nextcharacter in the phoneme stream PS_(C+1) at Step 462 and the processresumes at Step 408.

If PS_(C) is equal to the Data separator character at Step 408, at Step410, ST is inspected to determine if it is cleared. If ST is cleared, STis set to the numerical value of the content of CB in Step 412. If ST isnot cleared, at Step 414 ET is inspected to determine if it is cleared.If ET is cleared, ET is set to the numerical value of the content of CBin Step 416.

If ET is not cleared, at Step 418, PH is inspected to determine if it iscleared. Similarly, if PS_(c) is equal to Phoneme level data separator,at Step 428, at Step 418, PH is inspected to determine if it is cleared.If PH is cleared, PH is set to the value of the content of CB in Step420. If PH is not cleared, at Step 422, PB is inspected to determine ifit is cleared. If PB is cleared, PB is set to the numerical value of thecontent of CB in Step 424.

If PB is not cleared, at Step 426 CI is set to the numerical value ofthe content of CB. In Step 434, the sub-process Process Search Paths iscalled at Step 502 in FIG. 5.

If PS_(C) is the Closing time-slice delimiter at Step 430, at Step 440ST and ET are cleared. As seen in Box 608 of FIG. 6, WP and PP arecomposed of a one-dimensional array of N TSrchPath SP_(1−N). In Step442, the current TSrchPath SP_(C) is set to the first TSrchPath in WPSP₁. In Step 444, it is determined if the current TSrchPath SP_(C) isbefore the last TSrchPath in WP SP_(N). Step 446 inspects if the memberof the structure TSrchPath mLastPromote LP_(C), as seen in Box 606 ofFIG. 6, of the current TSrchPath SP_(C) has a different value than IS.If LP_(C) is different than IS the current TSrchPath SP_(C) is removedfrom WP. In Step 450, the current TSrchPath SP_(C) is set to thefollowing TSrchPath SP_(C+1). In Step 452, PP is copied into WP. In Step545, PP is cleared. In Step 456, TSC is set to IS. In Step 458, IS isincreased by one. In Step 436, PH, PB, and CI are cleared.

If PS_(C) is not equal to Data separator at Step 408, Phoneme level dataseparator at Step 428, the Opening time-slice delimiter at Step 430 northe Closing time-slice delimiter at Step 432, PS_(C) is appended to CBat Step 458. In Step 438, CB is cleared. In Step 462, if PS_(C) is notPS_(N), the PS_(C) is advanced to the next character in the phonemestream PS_(C+1) at Step 464. The Phoneme Stream Analysis process is thenrepeated from Step 408 until PSC is equal to PS_(N), at which point thePhoneme Stream Analysis process ends at Step 466 with WL that containsthe one dimensional array of TReco structures TR_(1−N) of TReco, as seenin Box 602 of FIG. 6, corresponding to the probable words that wererecognized from the phoneme stream PS_(1−N).

FIG. 5 depicts a flow scheme for a Phoneme Stream Analysis sub-processnamed Process Search Paths in the preferred embodiment of the invention.The Process Search Paths sub-process is part of the Phoneme StreamAnalysis process explained in FIG. 4 and is invoked from Step 434 inFIG. 4. The Process Search Paths sub-process modifies Working Paths,Promoted Paths, Words List and Bridge List provided Phoneme, Start Time,End Time, Probability, Cluster Index, Index in Stream and Dictionary.The main goal of the Process Search Paths sub-process is to populateWords List with all possible TReco that can be detected from allcombinations of phonemes in the phoneme stream—passed one phoneme at atime—until all phonemes in the phoneme stream are processed. The ProcessSearch Paths sub-process is called for each phoneme in a time-slice andthat phoneme is appended to mPhonemeStream of all existing search paths,and only search paths where mPhonemeStream result in a partial orcomplete pronunciation in the dictionary are copied in Promoted PathsPP.

In Box 502, a Process Search Paths sub-process is called from Step 434in FIG. 4.

Each TSrchPath structure in WP and PP contains a partially formed validpronunciation in mPhonemeStream. By partially formed validpronunciation, it is meant that mPhonemeStream in each TSrchPathcontains the beginning of pronunciation related to a word in thedictionary IND, but do not hold yet the full pronunciation required inorder to add a word in WL. The fact that TSrchPath structures reside inWP instead of PP means that they are part of the working set used inorder to extract the ones that can be promoted—in which case theTSrchPath in WP that can be promoted is duplicated in PP. The TSrchPathstructures in PP are the ones that were promoted for the currenttime-slice IS. Once a phoneme stream time-slice, delimited by theClosing time-slice delimiter in the phoneme stream, has been completelyanalyzed for all possible phonemes, all TSrchPath from PP are copied toWP (as seen in Step 452 of FIG. 4), and the Promoted Paths of thecurrent time-slice becomes the Working Paths of the followingtime-slice.

In Step 504, the current TSrchPath SP_(C) is set to the first TSrchPathSP₁ in WP. In Step 506, mCluster value in SP_(C) is inspected todetermine if it is the same value as CI. If the values are not the samein Step 506, Step 514 determines if SP_(C) is prior to SP_(N) in WP. IfSP_(C) is prior to SP_(N) in Step 514, Step 516 sets SP_(C) to the nextTSrchPath SP_(C+1).

If mCluster in SP_(C) is the same value as CI in Step 506, Step 508 setsStart Position STP to mPosition in SP_(C) and the Dictionary Forwardsub-process at Step 814 of FIG. 8 is called in Step 510. Followingcompletion of the Dictionary Forward sub-process, New Position NP isinspected to determine if it is cleared in Step 510. If NP was clear atStep 510, Step 512 inspects WP to determine if there is any TSrchPathafter the current TSrchPath. If there is any TSrchPath after the currentTSrchPath, Step 514 makes the following TSrchPath from the currentTSrchPath the current TSrchPath. Step 512 is re-invoked until SP_(C) isSP_(N) in WP.

If NP was not cleared at Step 510, Step 516 defines a Path to PromotePtP TSrchPath variable and sets it to a new cleared TSrchPath. In Step518, the content of SP_(C) is duplicated into PtP. Step 520 appends PHto mPhonemeStream in PtP. In Step 522, mToTime in PtP is set to ET. Step524 increments the value of mScore in PtP by PB. In Step 526, mPositionin PtP is set to NP returned by the call of the sub-process DictionaryForward in Step 510. Step 530 calls the sub-process Promote Path at Step622 of FIG. 6. Step 512 is then reprocessed until SP_(C) is SP_(N) inWP.

Once Step 512 determines that SP_(C) is SP_(N) in WP, Step 532 sets STPto mTopNode in IND and the Dictionary Forward sub-process at Step 814 ofFIG. 8 is called in Step 534.

In Step 536, NP set from the sub-process invoked at Step 532 isinspected to determine if it is clear. If NP is clear at Step 536, Step572 resumes the process following Step 434 in FIG. 4.

If NP is not clear, a new defined logical variable Check Bridging CB isset to false at Step 538. In Step 540, PtP is set to a new clearedTSrchPath. Step 542 sets mStartStream in PtP to IS. In Step 544,mPhonemeStream in PtP is cleared.

In Step 546, PH is appended at the end of mPhonemeStream in PtP. Step548 sets mPosition in PtP to NP set by the Dictionary Forwardsub-process in Step 534. Step 550 sets mFromTime in PtP to ST. In Step552, mToTime in PtP is set to ET. Step 554 sets mCluster in PtP to CI.In Step 556, mScore in PtP is set to PB. Step 558 calls the sub-processPromote Path at Step 622 in FIG. 6.

In Step 560, the value of Check Bridging CB is inspected. If CB isfalse, it is set to true at Step 562. Step 564 inspects thetwo-dimensional array of logical BL at the entry that corresponds to thephoneme index in stream at IS and the phoneme value PH. If that value isfalse, there is no bridging for that case and the process resumesfollowing Step 434 in FIG. 4.

If the value at Step 564 is true, then there is a bridging case to coverin the sub-process. In Step 566, PtP is set to a new cleared TSrchPathstructure. In Step 568, mPhonemeStream in PtP is set to the character‘+’. Step 570 sets mStartStream in PtP to IS incremented by one. Thesub-process then goes on and reprocesses Steps 546 to 560 as it didearlier. Step 560 will then confirm that CB is true and the process willresume following Step 434 in FIG. 4.

FIG. 6 depicts structure definitions as well as a flow scheme for twosub-processes used in the Phoneme Stream Analysis Process explained inFIG. 4 in the preferred embodiment of the invention. The sub-process GetStream Length is used in order to determine how many phonemes were usedin the provided phoneme stream Stream SM. This is useful since a phonemestream may have been the result of a bridging, and a single phoneme maybe shared between two different recognized words in the words list. TheGet Stream Length sub-process returns a number value in Stream Length SLthat represents how many phonemes were used in the given phoneme streamSM. The Promote Path sub-process is used in order to populate Words Listwith all TReco obtained from the search paths. While doing so, it usesBridge List in order to keep track of all phonemes and their positionswhich could affect the bridging of words.

In Box 602, a predetermined and programmed into the system TRecostructure is defined as a mSpelling value as a string that holds thespelling of the word, a mStream value that contains the phoneme streamuttered to produce the word as a string, a mCDScript value as a onedimensional array of string that holds Predicate Builder scripts laterrequired for conceptual analysis, a mCluster value as a number thatholds the cluster index that recognized the word, a mStartStream valueas a number that holds the phoneme index where the word was started tobe spoken in the utterance, a mEndStream value as a number that holdsthe phoneme index where the word was done being spoken in the utterance,a mPartOfSpeech value as a number holds the number value of the part ofspeech associated with the TReco, a mScore value as a number holds thecalculated score for the recognized word, a mFromTime value as a numberholds the starting time of the spoken word in the utterance, a mToTimevalue as a number holds the ending time of the spoken word in theutterance, a mRecoType value as a TRecoTp having two possible values(WORD_ENTRY or SYNTAX_ENTRY), the one-dimensional TReco array mChildrenholds the references to all children of the current TReco structure, andthe TTransient structure mTransient which is explained in FIG. 23.

In Box 604, a TRecoLst structure is defined. A TRecoLst is composed of amWordsList value as a one-dimensional array of TReco that contains allcandidate words built from every possible permutation of phonemes fromthe phoneme stream related to the utterance.

In Box 606, a TSrchPath structure is defined. A TSrchPath is composed ofa mPhonemeStream value as a string which contains the phoneme streamsuccessfully processed, a mCluster value as a number that holds thecluster index used to build the search path, a mScore value as a numberthat accumulates the score for each recognized phoneme in the searchpath, a mStartStream value as a number that holds the phoneme indexwhere the search path has began within the utterance, a mFromTime valueas number that holds the starting time within the spoken utterance, amToTime value as number that holds the ending time within the spokenutterance, a mPosition value as a TNodePos, as described in Box 810 ofFIG. 8, that holds the current position within the dictionary, and amLastPromote as a number that holds the phoneme index related to thelast promotion of the path.

In Box 608, a TPaths structure is defined. A TPaths is composed of amCollection value as a one-dimensional array of TSrchPath.

In Box 610, a TBridge structure is defined. A TBridge structure iscomposed of a mBridged value as a two dimensional array of logicalvalues. One dimension corresponds to the phoneme indexes in theutterance. The second dimension of the array corresponds to eachprobable spoken phoneme. This structure is used in order to hold theending phoneme flag for each possible ending phoneme index. If the word‘to’ (pronunciation ‘tu’) was spoken from phoneme index 5, the entryTBridge [6][(Number)‘u’] would be set to true identifying that a wordended at phoneme 6 (5+1) with the phoneme ‘u’ was recognized.

In Box 612, a Get Stream Length sub-process is called from Step 636 orStep 656 in FIG. 6 or Step 1170 in FIG. 11. The Get Stream Lengthsub-process associates a stream length to a stream while taking intoaccount the fact that pronunciation is built through bridging. As anexample “that text too”, the TReco holding “text” will have theassociated pronunciation “+text” since the ‘t’ phoneme was bridged withthe final ‘t’ of “that”. In which case, Get Stream Length sub-processwill have returned 3. Step 614 sets SL to the length of SM bydetermining how many characters are used in the phoneme stream SM. InStep 616, the first character of SM is inspected to determine if it is a‘+’ character. If the first character of SM is a ‘+’ character, SL issubtracted 2 at Step 618. The Get Stream Length sub-process resumesfollowing Step 636 or Step 656 of FIG. 6 or Step 1170 of FIG. 11,depending on which step called the sub-process, at Step 620.

In Box 622, the Promote Path sub-process is called from Step 530 or Step558 of FIG. 5.

In Step 624, mPosition in PtP is inspected to identify if it representsa terminated TNode. To be terminated means to have some mData contentassociated with the mNode of mPosition. If it is not terminated, thesub-process appends PtP to the end of PP at Step 678 and then resumesfollowing Step 530 or Step 558, depending on which step called thesub-process, of FIG. 5 at Step 680. If mPosition in PtP is terminated,the content of mData from mNode of mPosition in PtP is copied to a newvariable Data Dt as TData at Step 626. A TData structure, as describedin Box 804 of FIG. 8, holds N TWord TW_(1−N). In Step 628, the firstTWord TW₁ in Dt is set as the Current TWord TW_(C).

In Step 630 the variable Stream SM is set to mPhonemeStream in PtP andcalls the sub-process named Get Stream Length at Step 612 of FIG. 6 inStep 632. The Get Stream Length sub-process at Step 612 of FIG. 6 setsthe variable SL as a number with the result. In Step 634, the BL entryassociated with the phoneme index mStartStream in PtP plus SL and thevalue of the last phoneme in mPhonemeStream in PtP is set to true. Thatresults in identifying in BL that a word was recognized with thespecified ending phoneme at a given phoneme index within the utterance.A TWord structure may be associated with multiple parts of speechthrough the mPartOfSpeech logical array. This association is done when aDictionary IND is loaded into the system prior to the beginning of theprocess. The association of a spelling with pronunciation and parts ofspeech is predetermined and static through the use of the invention. InStep 636, the first mPartOfSpeech in TW_(C) entry in the array that isset to true is determined to be the current part of speech POS_(C). InStep 638, the sub-process determines if there is a POS_(C). If there isno POS_(C), meaning that all parts of speech were processed, the processmoves at Step 640. The sub-process then repeats Step 642 until TW_(C)reached TW_(N). In Step 640, the sub-process determines if TW_(C) isTW_(N). Step 642 sets TW_(C) to TW_(C+1) if required.

If there is a POS_(C) to process at Step 638, then a new TReco structureis created and put in the variable Recoed RC at Step 644. In Step 646,mSpelling in RC is set to mSpelling in TW_(C). In Step 648, mStream inRC is set to mPhonemeStream in PtP. In Step 650, mStartStream in RC isset to mStartStream in PtP. In Step 652, mCluster in RC is set tomCluster in PtP. Step 654 sets SM to mPhonemeStream in PtP. In Step 656,the sub-process Get Stream Length at Step 612 of FIG. 6 is called. Thesub-process Get Stream Length at Step 612 of FIG. 6 sets the variable SLas a number on output. In Step 658, mEndStream in RC is set tomStartStream in RC plus SL. In Step 660, mScore in RC is set to mScorein PtP divided by SL. In Step 662, all mCDScript related to current partof Speech from TW_(c) are copied to mCDScript array in RC. Auto-scriptPredicate Builder scripts associated with the current part of speech, asexplained in FIG. 17, would also get copied to an element of mCDScriptarray in RC. In Step 664, mFromTime in RC is set to mFromTime in PtP. InStep 666, mToTime in RC is set to mToTime in PtP. In Step 668, mRecoTypein RC is set to WORD_ENTRY. In Step 670, mExtra in Recoed is set to themExtra element, if any, corresponding to POS_(C) in TW_(C). In Step 672,mLastPromote in PtP is set to Index in Stream IS. In Step 674, theFlatten Scripts sub-process at Step 702 in FIG. 7 is called. In Step676, the next part of speech that is set to true POS_(C+1) following thecurrent part of speech POS_(C) is set to become the current part ofspeech POS_(C). Step 638 is then re-invoked until all parts of speech inTW_(C) are processed. The process returns to Step 530 or Step 558 ofFIG. 5, depending on which step called the sub-process.

FIG. 7 depicts a flow scheme for the Flatten Scripts sub-process in thepreferred embodiment of the invention. Every TReco structure may beassociated with multiple Predicate Builder scripts through theone-dimensional array of string mCDScript. This is not practical for thealgorithm associated with the conceptual analysis process since it wouldmean applying the algorithm on multiple possible permutations for eachTReco structure, consequently complicating logics significantly. Insteadof doing so, any TReco structure containing multiple string in itsmCDScript is duplicated as many times as it has strings, and isassociated only one string in mCDScript. That will mean that a TRecostructure only has a single string in mCDScript instead of a completeone-dimensional array of string. Consequently, there shall be no needfor permutations of Predicate Builder scripts stored in mCDScript in theConceptual Analysis process later described.

In Step 702, the Flatten Scripts sub-process is called from Step 674 inFIG. 6. Step 704 inspects mCDScript in RC to determine if there is morethan one string contained in it. Step 706 sets the current script S_(C)to the last string in mCDScript of RC. Step 708 creates a new TRecovariable Recoed Copy RCP. Step 710 copies the content of RC in RCP. Step712 removes all strings contained in mCDScript from RCP. Step 714 addsSc to mCDScript in RCP. Step 716 removes S_(C) from mCDScript in RC.Step 718 adds the TReco structure RCP to WL.

If Step 704 determines that there is not more than 1 mCDScript stringcontained in it, Step 720 inspects mCDScript to determine if the isexactly 1 string in it. If yes, in Step 722, RC is added to WL. If no,the sub-process proceeds to Step 724. Step 724 resumes the processfollowing Step 674 in FIG. 6.

FIG. 8 depicts a flow scheme for a Dictionary Forward sub-process aswell as structure definitions related to the dictionary in the preferredembodiment of the invention. The Dictionary Forward sub-process is analgorithm to perform an index search provided the phoneme stream primaryindex and resulting with a TNodePos structure which identifies theposition within a node in the index tree. The primary index is definedas the unique entrant required in the organized elements in memory orstored memory, called the dictionary, in order to retrieve dataassociated with it. For this invention, the primary index are phonemesthat comprise the phoneme stream that is a pronunciation for a givenword.

The invention often refers to a unique dictionary IND that stores allwords W_(1−N) and corresponding pronunciations PSI_(1−N) which may berecognized from speech. In order to extract a word W_(i) from thedictionary, a phoneme stream corresponding to the pronunciation of thatword PSI_(i) is required. Each Word W_(i) comprises at least onepronunciation PSI_(i), which itself comprises N phonemes Ph_(1−N). Inorder to find the node where a word W_(i) resides, the DictionaryForward sub-process needs to be invoked for each phoneme Ph_(i) thatcomprises the phoneme stream PSI_(i) while setting Start Position STP asthe result of the previous invocation of the Dictionary Forwardsub-process, i.e. the result for the previous phoneme Ph_(i−1), or acleared STP for the first invocation of a new TSrchPath. IND also storesother related data for each word W_(i) and pronunciation PSI_(i), asseen in Box 802, 804 and 806, such as, for example, parts of speechPOS_(i,1−N) associated with a word W_(i), and all Predicate Builderscripts CD_(i,1−N) associated with a word W_(i) and part of speechPOS_(i,j).

The preferred method for extracting data related to a givenpronunciation PSI_(i) of a Word W_(i) in the dictionary IND is describedin FIG. 8, but, the indexing method may be one of many known today tothose skilled in the art. By way of example and not intending to limitthe invention in any manner, the indexing method used may also includesequential searching, searching an ordered table, binary tree searching,balanced tree searching, multi-way tree searching, digital searching,hashing table searching or any other adequate indexing and dataretrieval process now known or later developed by those skilled in theart.

In Box 802, a predetermined and programmed into the system TWordstructure is defined as a string mSpelling containing the spelling ofthe word W_(i), a one dimension array of logical values mPartOfSpeech, atwo-dimensional array of string mCDScript, and a one-dimensional arrayof string mExtra. mExtra is synchronized with the mPartOfSpeech array.mExtra is generally empty, but will contain extra information fortargeted parts of speech. As an example, the word with the spelling“one” will contain the extra data “1” associated with the part of speechCARDINAL_NUMBER to identify the numerical equivalence.

The invention requires the definition of parts of speech to perform.Parts of speech POS are syntactic related nature of words that are usedin the invention. Each part of speech is associated a unique andconstant value. Predefined parts of speech and their respective valuesfor the English implementation of the invention can be designated asfollow:

-   -   UNKNOWN=0, NOUN=1, PLURAL=2, PROPER_NOUN=3, NOUN_PHRASE=4,        VERB_USU_PARTICIPLE=5, VERB_TRANSITIVE=6, VERB_INTRANSITIVE=7,        VERB=8, ADJECTIVE=9, ADVERB=10, CONJUNCTION=11, PREPOSITION=12,        INTERJECTION=13, PRONOUN=14, WH_PRONOUN=15, DEFINITE_ARTICLE=16,        INDEFINITE_ARTICLE=17, ORDINAL_NUMBER=18, CARDINAL_NUMBER=19,        DATE=20, TIME=21, QUANTIFER=22, ADJECTUVE_PHRASE=23,        PREPOSITION_PHRASE=24, VERB_PHRASE=25, WH_NP=26, AUX=27,        GERUNDIVE_VERB=28, GERUNDIVE_PHRASE=29, REL_CLAUSE=30 and        SENTENCE=31.

The invention also allows for dynamic definition of new parts of speech.Through transform scripts, as explained in FIGS. 15 and 16, and shown inFIG. 9, a new part of speech can be included in any transform script. Asshown in FIG. 9D, new parts of speech like AIRLINE, FLIGHT, FLIGHTS,GATE or CITY are defined by introducing them in any relevant transformscript line after the Affectation identifier (‘->’). Each logical valuewithin mPartOfSpeech array identifies if any given word W_(i) isassociated a specific part of speech POS_(i) corresponding to the valueat the index (true=associated, false=not associated) of the numericalvalue of POS_(i). For the word ‘James’, mPartOfSpeech[3] is true(identifying that word is associated the POS PROPER_NOUN) and everyother entry would typically be false.

In CD_(i), Predicate Builder scripts are stored for each associated partof speech POS_(i,j). CD_(i) is a two-dimensional array since any givenword may hold multiple Predicate Builder scripts for any associated partof speech POS_(i,j) (this relates to the reality that any given word mayhave multiple meanings). For the TWord W_(i) holding the spelling‘James’, mCDScript[3][1] will hold a Predicate Builder script thatidentifies a person named ‘James’ and every other entry of mCDScriptwould typically not hold any content.

Any given character can be associated a unique numerical value so thatan ordered sequence of characters enables the system to comparecharacters on their numerical equivalence. By way of example and notintending to limit the invention in any manner, the ASCII index value,Unicode index related value, multi-byte number related value, or anyother way of associating a numerical value to a character, well known tothose skilled in the art, can be used to associate a predeterminedunique numerical value to any character.

In Box 804, a predetermined and programmed into the system TDatastructure is defined as a one-dimensional array of TWord structuresmWords. Each TData structure is kept in a TNode ND_(i) and is what holdsthe information of the dictionary IND associated with the node ND_(i)when a Dictionary Forward sub-process potentially sets a non-clearedTNodePos in New Position NP.

In Box 806, a predetermined and programmed into the system TNodestructure is defined as a string mIndexPart, a TNode mParentNode,mEqualNode, mSmallerNode and mGreaterNode and the TData mData. A clearedmData, i.e. a mData that does not contain any TWord, identifies anon-terminated TNode. Any TNode with a mData that is not cleared, i.e. amData that contains at least one TWord, identifies a terminated TNode. Aterminated TNode ND_(i) can also be linked to other terminated and/ornon-terminated TNode ND_(j). As shown in Box 812, each TNode structureresiding in memory or stored memory needs to be related to each other insuch a way that the organized TNode tree, called the TIndex, can be usedto extract any W_(i) provided a pronunciation PSI_(i).

In Box 808, a predetermined and programmed into the system TIndexstructure is defined as a TNode mTopNode and a number mNodeCount to holdthe total number of TNode in the given TIndex. By way of example and notintending to limit the invention in any manner, in the context of thisinvention only one Tindex, called the dictionary IND, is required,although multiple Tindex may be used so long as data related to anygiven word W_(i) and its related pronunciation PSI_(i) can be extractedthrough an indexing system with multiple Tindex or equivalent indexingmethod.

In Box 810, a predetermined and programmed into the system TNodePosstructure is defined as a TNode mNode and a number mindexInNode. TheTNodePos structure is used in order to keep track of any position withinthe TIndex tree structure. By keeping the TNode mNode and mindexInNodeto refer to positions within the TNode ND_(i) that may hold mIndexPartthat are more than a single character, as seen in Box 812, it becomespossible to refer to any position within the TIndex tree structurewithout further requirement.

In Box 812, a Tindex tree structure example is shown with some contentto help understanding. As primary index in Box 812 some spellings areused instead of pronunciations (spellings ‘no’, ‘did’, ‘do’, ‘nest’,‘nesting’, ‘to’, ‘node’, ‘null’ and ‘void’). Each terminated TNodecorresponding to associated spelling contains the TData d0 . . . d8. Theprocess to extract data from such TIndex tree structure is explained inthe Dictionary Forward sub-process, although in practice the primaryindex used in the invention are phoneme streams instead of spellings.

It is the programming engineer's responsibility to create such indexingstructure as the one described in Box 812, or any other equivalentindexing structure, in order for the algorithm described in theDictionary Forward sub-process to execute as described. Population ofsuch indexing structure, or equivalent structure, is a task that iscommon to those skilled in the art and does not require furtherexplanation in this application.

In Box 814, the Dictionary Forward sub-process is called from Step 510or Step 534 in FIG. 5.

In Step 816, STP is inspected to identify if it is cleared. If it iscleared, mNode of STP is set to mTopNode in IND and mIndexInNode in STPis set to 0 at Step 818. In Step 820, the character pointed to bySTP—character index mIndexInNode of the string mIndexPart of mNode inSTP—is tested to determine if it is the same as PH. If PH is not thesame as the character pointed to by STP at Step 820, mNode in STP isinspected at Step 828 to determine if mIndexInNode is equal to the lastcharacter index mIndexPart of mNode in STP. If it is not the lastcharacter, NP is cleared in Step 832 and the Dictionary Forwardsub-process resumes following Step 510 or Step 534, depending on whichstep called the sub-process, of FIG. 5 at Step 844. If mIndexInNode inSTP is the last character mIndexPart of mNode in STP, the processinvokes Step 834.

In Step 834, the character pointed to by STP is inspected to identify ifit is smaller than PH, i.e. if the ASCII index value of the characterpointed to by STP is smaller than the ASCII index value of the characterPH as an example. If it is smaller, the process invokes Step 838 wheremSmallerNode of mNode in STP is inspected to identify if it is cleared.mSmallerNode of mNode in STP is assumed to be cleared if it does nothold a TNode value. If it is cleared, NP is cleared at Step 832 and theprocess resumes following Step 510 or Step 534, depending on which stepcalled the sub-process, of FIG. 5 at Step 844. If it is not cleared,that is, mSmallerNode of mNode in STP holds a TNode value, mnode of STPis set to mSmallerNode of mNode in STP and mIndexInNode in STP is set to0.

The Dictionary Forward sub-process then re-invokes Step 820. In Step834, if the character pointed to by STP is not smaller than PH, Step 838is invoked. Character comparison is performed the same way as in Step834 where a unique numerical value—the ASCII index value as anexample—associated with a given character is compared to thecorresponding unique numerical value associated with the othercharacter. In Step 836, it is assumed that the character pointed to bySTP is greater than PH (since the equal and smaller than tests bothfailed). Step 836 inspects mGreaterNode of mNode in STP to identify ifit is cleared. mGreaterNode is assumed to be clear if it does not hold aTNode value. If it is cleared, NP is cleared in Step 832 and the processresumes following Step 510 or Step 534, depending on which step calledthe sub-process, of FIG. 5 at Step 844. If it is not cleared, that is,mGreaterNode of mNode in STP holds a TNode value, mNode in STP is set tomGreaterNode of mNode in STP and mIndexInNode in STP is set to 0.

If, in Step 820, the character pointed to by STP is the same as PH, bycomparing the ASCII index values as an example, Step 822 is invoked. InStep 822, STP is inspected to identify if it points to the lastcharacter of mIndexPart from its mNode. If there are more charactersafter the character pointed to by STP, mIndexInNode in STP isincremented by 1 at Step 826. If there are no more characters after thecharacter pointed to by STP, mNode in STP is set to mEqualNode of mNodein STP at Step 824. In either case, Step 830 is invoked where NP is setto STP and then the process resumes following Step 510 or Step 534,depending on which step called the sub-process, of FIG. 5 at Step 844.

Once a list of probable words has been determined from the PhonemeStream Analysis, syntactic rules, or transform scripts, are applied toform a list of syntactically correct sequences of words from thosewords.

Syntactic Analysis

FIG. 9 describes the content of transform scripts used in the preferredembodiment of the invention. By way of example and not intending tolimit the invention in any manner, FIG. 9A, 9B and 9C describe sometransform scripts that can handle the English language, and FIG. 9Ddescribes a transform script that can be used in the English language inorder to build an airline response system. The invention does notpretend to limit itself to the English language applied for airlineresponse systems in any way even though this application documents asystem that handles utterances for the English language mostly in thecontext of an airline response system. Also, the syntax used in order tointerpret transform scripts is only provided as an example, and there isno intention to limit the invention in any manner.

A programming engineer is free to modify, produce or not use new orexisting transform scripts based on the needs of his implementation. Hisdecision should be driven by the requirements related to system'simplementation. Transform scripts should be produced by programmingengineers that are knowledgeable in the field of linguistics.

The purpose of transform scripts is to describe the rules related topermutation analysis of streams so that sequences of streams—or TReco,since in this application streams refer to TReco structures—that respectsuch rules can be produced. Phoneme Stream Analysis produced an array ofTReco in WL by permuting all recognized phonemes over a predefinedthreshold. Each of the TReco within WL has an associated mStartStream,which may or may not be different from other TReco. Transform scriptsresponsibilities are to produce sequences of streams that respect rulesstated in it and also respect pronunciation boundaries—i.e. when didthey start in the phoneme stream (mStartStream) and where did they endin the phoneme stream (mEndStream). Transform scripts typically resideon file on disk and are loaded in memory in such a way that transformscript interpretation is optimized for speed. That means setting somestructures in memory at load time so that all elements of informationrelated to permutation analysis are already in place for swiftprocessing at interpretation time. Respecting pronunciation boundariesmeans that location of where the TReco was recognized in the phonemestream needs to be consistent between each TReco in a sequence produced.As an example, for the transform script line [“splitting”][“it”], whichstates that a TReco with mSpelling “splitting” followed by a TReco withmSpelling “it” would be a successful sequence. “splitting” also contains“it” (“Spl_it_ing”). Should no requirement for pronunciation boundarieshave been made, the utterance “splitting” would then succeed since bothspellings were in WL, “splitting” with a mStartStream of 1 and amEndStream 7, and “it” with a mStartStream of 4 although only one wordwas uttered.

By way of example and not intending to limit the invention in anymanner, the following syntax of transform scripts was selected in thepreferred embodiment of the system in order to extract from each linethe required information:

-   -   1. Stream delimiters (characters ‘[’ and ‘]’). To isolate        multiple streams from one another and produce sequences of        streams based on the transform script line, the Stream        delimiters are used. Between the Opening stream delimiter (‘[’)        and the Closing stream delimiter (‘]’) reside some criteria to        match for a stream. By way of example, and not intending to        limit the invention in any manner, possible criteria to match in        provided transform scripts example are parts of speech and        spellings.    -   2. Spelling identifier (sequence of characters between two        double quote characters). To match on spelling for a stream,        unrestricted spelling can be specified within Spelling        identifiers that also needs to be between the Opening and        Closing stream delimiter characters.    -   3. Conditional sequence identifier (opening and closing        parenthesis characters). To specify a conditional statement, a        single stream or a sequence of streams including their        corresponding stream delimiters may be enclosed within the        Opening conditional sequence identifier (‘(’ character) and the        Closing conditional sequence identifier (‘)’ character). By way        of example, and not intending to limit the invention in any        manner, selected syntax in the preferred embodiment of the        invention uses a two-way decision syntax for conditional        sequence identifiers. A modified syntax and according algorithm        could as well implement a N-way decision syntax.    -   4. Partial spelling match identifier (‘_’ character). Preceding        or following a partial spelling within Spelling identifier        characters, the Partial spelling match identifier would identify        a ‘Start with’ or ‘Ends with’ spelling criteria requirement for        the stream.    -   5. Tag identifier (‘<’ and ‘>’ characters). Within Stream        delimiters, tags may be associated with each stream in a        sequence formed. The optional tag name needs to be provided        between the Opening tag identifier (‘<’ character) and the        Closing tag identifier (‘>’ character).    -   6. Tag delimiter (‘:’ character). After a tag has been        identified (using the Opening tag identifier, following by the        tag name and then the Closing tag identifier), a Tag delimiter        is found on the transform script line.    -   7. Line name separator (‘:’ character). After the optional line        name in a transform script line, the Line name separator is used        to separate the line name from the permutation described in the        transform script line.    -   8. Affectation identifier (‘->’ characters). Automatic        transforms (transforms that do not require the approval of a        call-back sub-process) are specified on a transform script line        by following the description of stream sequence criteria to        match with an Affectation identifier and a part of speech.    -   9. Criteria separators (‘&’ and ‘|’ characters). Between each        part of speech and spelling specified between Stream delimiters,        Criteria separators are used. Either ‘&’ or ‘|’ can be used as a        Criteria separator with equivalent result. Parts of speech and        spellings criteria are delimited by Criteria separators. Between        parts of speech and spellings, the ‘|’ Criteria separator is        typically used—stating that a stream needs to be a specified        part of speech or spelling or another one. After all parts of        speech specified and before the first spelling, the Criteria        separator ‘&’ is typically used—stating that not only parts of        speech criteria needs to be respected, but also (and) spelling        criteria.    -   10. Comment identifier (‘#’). Anywhere in a transform script        line, comments may be added if preceded by the Comment        identifier.        Syntax used in transform scripts, and provided as an example, is        as follow:    -   1. Stream criteria to match needs to be between the Opening and        closing stream delimiters. Parts of speech to match needs to be        stated prior, if necessary, to spelling related matching. As an        example, ‘[VERB & “is”]’ identifies any TReco within WL that is        a VERB part of speech and that has a mSpelling “is”.    -   2. Every optional stream criteria match needs to be stated        between the Opening and Closing conditional sequence        identifiers. As an example, ([ADVERB])[ADJECTIVE] identifies a        sequence of streams where an optional ADVERB part of speech in        mPartOfSpeech of a TReco in WL is followed by a mandatory        ADJECTIVE part of speech in mPartOfSpeech of a TReco in WL while        respecting pronunciations boundaries between the two TReco        structures. Note that a stream sequence of a single TReco with        mPartOfSpeech ADJECTIVE is also valid for ([ADVERB])[ADJECTIVE]        since the ADVERB part of speech is optional.    -   3. Spelling match that starts with the Partial spelling match        identifier identifies an ‘ends with’ spelling stream matching        criteria. As an example, [VERB & “ming”] could match the VERB        mPartOfSpeech in a TReco where the spelling is “running” since        “running” ends with the characters “ing”. The invention may as        well signal an end with spelling match with the same syntax. As        an example, [VERB & “run_”] could match the VERB mPartOfSpeech        in a TReco where the spelling is “running” since “running”        starts with the characters “run”.    -   4. Transform script lines that do not include the Affectation        identifier are to be processed by a call-back sub-process.        Call-backs are required for more complex transformation rules in        some cases. As an example, the stream sequence ‘fifty one’ is        allowed in order to build an ORDINAL_NUMBER that is 51. That        stream sequence is an ORDINAL_NUMBER that follows another        ORDINAL_NUMBER. However, the stream sequence ‘one fifty’ is not        a valid one in order to generate an ORDINAL_NUMBER, but it is        also a stream sequence of an ORDINAL_NUMBER that follows another        ORDINAL_NUMBER. Consequently, for some transform scripts, when        sequences are matched, instead of transforming automatically the        sequence in a new part of speech, a call-back sub-process is        called and it may or may not proceed with the transformation.    -   5. Tags are between the Opening and Closing tag identifiers.        Tags are specified in order to facilitate content extraction        within transform script call-back sub-processes (as can be seen        in FIGS. 11 and 12).    -   6. Following the optional Affectation identifier in transform        scripts is a part of speech that a new TReco structure enclosing        all TReco used to form it (spelling that is cumulative of all        TReco used from the transform script) with a mPartOfSpeech that        corresponds to the part of speech after the Affectation        identifier. As an example, [VERB & “ing”]->GERUNDIVE_VERB would        create a new TReco structure in WL for all the VERB parts of        speech which spelling ends with “ing”—like “running”, “falling”,        etc. Note that the part of speech on the right of the        Affectation identifier does not need to be a pre-programmed part        of speech. As an example, in FIG. 9D, [NOUN & “gate”] ([NOUN &        “number”])[<GATE NUMBER>:CARDINAL_NUMBER|ORDINAL_NUMBER]->GATE.        The part of speech GATE is not pre-programmed into the system,        but is allowed in a transform script line, and will consequently        be added to the list of possible parts of speech and be treated        equally as the pre-programmed parts of speech. The spoken        sequences “gate number twenty two”, “gate fifty one” or “gate        twenty third” would then generate a new TReco structure in WL        that has a mPartOfSpeech GATE. The programming engineer is free        to modify, add or delete transform scripts at his convenience        depending on the needs that are targeted to be covered. In this        case, the GATE part of speech was introduced for the purpose of        a hypothetical flight response system and may well not be        adequate for other needs, meaning that deletion of the transform        script line would be relevant.

FIG. 9A describes an example of transform script content used to performsyntactic transforms for the English language in the preferredembodiment of the invention.

FIG. 9B describes an example of transform script content used to performnumeric transforms for the English language in the preferred embodimentof the invention. Since there is no Affectation identifier, a call-backsub-process needs to be specified for the transform script to be handledproperly. The Number Producer Permutation Callback (described in FIG.14) is used for that purpose. The transform script in FIG. 9B and theNumber Producer Permutation Callback handle sequences like “One hundredtwenty five”, “two hundred twenty third” or “one million and one hundredforty eight thousand and three hundred fifty three” and create a TRecostructure with the corresponding number associated to the sequencedstream.

FIG. 9C describes an example of transform script content used to performtime transforms for the English language in the preferred embodiment ofthe invention. Since there is no Affectation identifier, a call-backsub-process needs to be specified for the transform script to be handledproperly. The Time Producer Permutation Callback (described in FIG. 13)is used for that purpose. The transform script in FIG. 9C and the TimeProducer Permutation Callback handle sequences like “four thirty pm”,“fifteen to one am” or “eight o'clock” and create a TReco structure withthe corresponding time associated to the sequenced stream.

FIG. 9D describes an example of transform script content used to build acustom airline response system for the English language in the preferredembodiment of the invention. That transform script interpreted after thetransform scripts in FIG. 9B and FIG. 9C but before the transform scriptin FIG. 9A (as seen in the Syntactic Analysis process in FIG. 10)generates, as one of many things, SENTENCE parts of speech fromutterances like “when is flight one hundred and twenty two arriving”,“how late is flight one hundred twenty two” or “is American airlineflight number six hundred arrived yet”.

FIG. 10 depicts a flow scheme for the Process Script Files sub-processesas well as the Syntactic Analysis process in the preferred embodiment ofthe invention. By way of example and not intending to limit theinvention in any manner, the Syntactic Analysis process is a simpleBottom-Up parsing process, well known to those skilled in the art (asfirst suggested by Yngve in 1955 and perfected by Aho and Ullman in1972), but could as well be implemented as a Top-Down, an Early, afinite-state, a CYK parsing process, well known to those skilled in theart, or any other adequate parsing process now known or later developedwhich shall result in obtaining comparable outcome although performancemay vary depending on the parsing method chosen.

The purpose of the Syntactic Analysis process is to populate theTRecoLst Words List WL variable with TReco structures based on the rulesstated in all transform scripts as shown in FIG. 9. The entrant to theSyntactic Analysis process is the TRecoLst variable WL built in thePhoneme Stream Analysis process in FIG. 4, and the output of theSyntactic Analysis process is also the transformed TRecoLst variable WL.The Process Script Files sub-process at Step 1002 in FIG. 10 is used inthe Syntactic Analysis process in order to process each loaded scriptfile sequentially.

In Box 1002, the Process Script Files sub-process is called from Step1054 in FIG. 10.

Scripts List SL has N TScript S_(1−N). In Step 1004, the current TScriptS_(C) in SL is set to S₁. Step 1006 determines if S_(C) is or is priorto S_(N). S_(C) has N TScptLine L_(1−N) Step 1008 sets the currentTScptLine L_(C) to L₁ in S_(C). Step 1010 sets Index in Stream IS to theindex of the first phoneme in the phoneme stream PS₁. The phoneme streamPS is the output from the phoneme recognition process as seen in FIG. 2.

In Step 1012, the logical variable Reprocess RP is set to false. RP maybe changed to true by any sub-process, in which case it would identifythat the current line L_(C) needs to be reprocessed. That relates torecursive transform script lines. That is, a transform script line mayperform an automatic transform script transformation of a part of speechPOS_(i) into a part of speech POS_(i). Should there be at least onetransformation performed from such a transform script line, it isimportant to reprocess the transform script line since there is a newstream with the part of speech POS_(i) that was not analyzed in thefirst pass (the one that was actually created at the first pass). Suchreprocessing is performed until no more streams are created from theinterpretation of the transform script line.

In Step 1014, IS is inspected to determine if it is prior to the end ofPS. PS contains 1-N time-slices. IS can't exceed the Nth time-slice inPS.

In Step 1016, Reprocess is inspected to determine if it is true. Step1018 verifies if L_(C) is L_(N). Step 1020 sets L_(C) to L_(C+1). Step1022 sets S_(C) to S_(C+1).

In Step 1024, a new one-dimensional array of TReco Partial PT iscleared. Step 1026 sets LN to L_(C) and sets SCR to S_(C). Step 1028calls the sub-process Link Sequences Stream at Step 1102 in FIG. 11.Step 1030 increments IS by one. Step 1032 resumes the process followingStep 1054 in FIG. 10.

In Step 1034, the Syntactic Analysis process is started with the entrantTRecoLst Words List from the Phoneme Stream Analysis process explainedin FIG. 4. Step 1036 clears a new one-dimensional array of TScriptvariable Scripts List SL.

In Step 1038, SF is set to the File variable Number Transform Scriptfrom FIG. 9B, CB is set to Step 1402 in FIG. 14. In Step 1040, thesub-process Load Script File is called. Upon loading of the File, SLwill have a new TScript element added to it containing the TScriptrelated to the File as seen in Step 1588 of FIG. 15.

In Step 1042, SF is set to the File variable Time Transform Script fromFIG. 9C, CB is set to Step 1302 in FIG. 13. In Step 1044, thesub-process Load Script File is called. Upon loading of the File, SLwill have a new TScript element added to it containing the TScriptrelated to the File as seen in Step 1588 of FIG. 15.

In Step 1046, SF is set to the File variable Custom Transform Scriptfrom FIG. 9D, CB is cleared. In Step 1048, the sub-process Load ScriptFile is called. Upon loading of the File, SL will have a new TScriptelement added to it containing the TScript related to the File as seenin Step 1588 of FIG. 15.

In Step 1050, SF is set to the File variable Syntactic Transform Scriptfrom FIG. 9A, CB is cleared. In Step 1052, the sub-process Load ScriptFile is called. Upon loading of the File, SL will have a new TScriptelement added to it containing the TScript related to the File as seenin Step 1588 of FIG. 15.

In Step 1054, the sub-process Process Script Files is called with thevariables SL and WDS.

Step 1056 terminates the Syntactic Analysis process. The result of theSyntactic Analysis process is to populate the list WL with TRecostructures that obey the rules in the transform scripts for ConceptualAnalysis to process them.

FIG. 11 depicts a flow scheme for a Link Sequences Stream Sub-process inthe preferred embodiment of the invention. The Link Sequences StreamSub-process is part of the Syntactic Analysis process and its functionis to extract from the TRecoLst Words passed to it, TReco structuresthat can be linked to the previous TReco structure in Partial so thatvalid syntactic sequences of words are gradually built while respectingword pronunciations boundaries.

In Box 1102, the Link Sequences Stream sub-process is called from Step1028 in FIG. 10 or Step 1174 in FIG. 11.

In Step 1104, PT is inspected to determine if there is at least oneTReco in it. If not, Step 1106 clears the character variable BridgeLetter BL. If yes, Step 1108 sets BL to the last phoneme of mStream ofthe last TReco in PT.

W_(L) contains N TReco W_(1−N). In Step 1110, the current TReco W_(C) isset to W₁ in W_(L). Step 1112 determines if W_(C) is or is before W_(N).In Step 1114, mStartStream in W_(C) is inspected to determine if it isequal to IS. Step 1116 makes W_(C+1) the current TReco W_(C) and thesub-process returns to Step 1112 to determine if W_(C) is or is beforeW_(N).

If mStartStream in W_(C) is equal to IS in Step 1114, in Step 1118,mStream is inspected in W_(C) to determine if it starts with thecharacter ‘+’. If true, Step 1120 verifies if the second character ofmStream in W_(C)—the character after ‘+’—is the same as BL. If true, inStep 1122, WRD is set to W_(C). If not true at Step 1118, thesub-process proceeds directly to Step 1122 and WRD is set to W_(C).

In Step 1124 the sub-process Test Stream at Step 1202 in FIG. 12 iscalled. Step 1126 verifies if PS or FS is true. The PS and FS logicalvalues are set in Test Stream sub-process. PS with a value of trueidentifies that SM is valid (respects the rules stated in LI) and that apartial sequence can be formed with SM. FS with a value of trueidentifies that SM is valid (respects the rules stated in LI) and that afull sequence can be formed with SM. Both PS and FS are independent ofeach other, meaning that detection of a partial sequence is not relatedto the detection of a full sequence and the other way around. If both PSand FS are false, SM can't be used while respecting the rules stated inLI.

In Step 1128, a new one-dimensional array of TReco variable Keep PartialKPT is set to PT. Step 1130 sets a new one-dimensional array of stringvariable Keep Work Perm KWP to mWorkPerm of mPermutationLst in LI. Step1132 appends W_(C) to PT as a new element in the one-dimensional array.

In Step 1134, FS is inspected to determine if it is true. Step 1136inspects mCallback in SPT to determine if it is clear. If mCallback inSPT is not clear at Step 1136, then the sub-process proceeds to Step1166.

If mCallback in SPT is clear at Step 1136, then in Step 1138 a new TRecovariable Reco RC is defined. Step 1140 sets mChildren in RC to Partial.Step 42 sets mSpelling in RC to the string that is formed byconcatenating all mSpelling in all TReco of PT from the first to thelast one and putting a space character between each of them. Step 1144sets mStream of RC to the string that is formed by concatenating allmStream in all TReco of PT from the first to the last one. Step 1146sets mStartStream of RC to mStartStream of the first TReco in PT. Step1148 sets mEndStream of RC to mEndStream of last TReco in PT. Step 1150sets mPartOfSpeech of RC to mPOSTransform in LI. Step 1152 setsmFromTime in RC to mFromTime of first TReco in PT. Step 1154 setsmToTime in RC to mToTime of last TReco in PT. Step 1156 sets mRecoTp inRC to SYNTAX_ENTRY.

In Step 1158, WL is inspected to determine if RC is already in theone-dimensional array of TReco. Step 1160 adds RC to WL as a new elementin the array. Step 1162 inspects mRecursive in LI to determine if it istrue. Step 1164 sets the logical variable Reprocess to true. Thevariable Reprocess is inspected in FIG. 10, and as true value statesthat the TScptLine needs to be re-evaluated. If RC is already in theone-dimensional array of TReco at Step 1158, the sub-process proceeds toStep 1166.

In Step 1166, PS is inspected to determine if it is true. If yes, Step1168 sets SM to mStream in WC. Step 1170 calls the sub-process GetStream Length at Step 612 of FIG. 6. Step 1172 adds the value returnedfrom the sub-process Get Stream Length at Step 1202 in FIG. 12 SL to IS.

In Step 1174, the Link Sequences Stream sub-process at Step 1102 in FIG.11 is called, and the partial sequence is processed in the LinkSequences Stream sub-process to determine if other permutations may bedetected starting with the partial sequence. Step 1176 sets PT to KPT.If PS is not true at Step 1166, the sub-process proceeds directly fromStep 1166 to Step 1176. Step 1178 sets mWorkPerm of mPermutationLst inLI to KWP. As Step 1116 forwards the current TReco in WL, it shallprocess all TReco structures in WL, at which point Step 1180 resumes theprocess following Step 1028 in FIG. 10 or Step 1174 in FIG. 11 dependingon which step called the Link Sequences Stream sub-process.

FIG. 12 depicts a flow scheme for a Test Stream sub-process in thepreferred embodiment of the invention. The Test Stream sub-process isinvoked in order to test a single TReco Word to identify if it respectsat least one remaining condition in the TScptLine Line so that a partialsequence and/or a full sequence could be completed, as returned from thesub-process in the logical Partial Sequence and Full Sequence.

In Box 1202, the Test Stream sub-process is called from Step 1124 inFIG. 11. It requires the TReco Word WRD to have been set by the caller,as well as TScptLine Line LI, TScript Script SPT, the one-dimensionalarray of TReco Partial PT and the TRecoLst Words List WL. Upontermination, the Test Stream sub-process sets two logical values:Partial Sequence PS and Full Sequence FS. PS signals that SM respectedthe rules stated in LI and that a partial sequence can consequently beformed with it. FS signals that SM respected the rules stated in LI andthat a full sequence can consequently be formed with it.

In Step 1204, PT is inspected to determine if it is empty—i.e. if itcontains a total of zero TReco. In Step 1206, mPermutation is copied tomWorkPerm of mPermutationLst in LI. All values in mCondRes and mCondBoolin mPermutationLst in LI are also cleared at Step 1208.

As seen in Box 1604 of FIG. 16, a TPermute has N TCondition CD_(1−N). InStep 1208, the current TCondition CD_(i) is set to CD₁ ofmPermutationLst in LI. Step 1210 verifies that there is a CD_(i).

In Step 1212, Result RS is set to false. Step 1214 inspects mPosTest inCD_(i) to determine if there is at least one value in the logicalone-dimensional array set to true. In Step 1216, the mPOSTest entryindex corresponding to the value associated with the Part of SpeechmPartOfSpeech in WRD is inspected to determine if it is true. Should themPartOfSpeech in WRD be VERB, as explained in FIG. 4, the valuemPOSTest[8] would be inspected since the numerical value associated withVERB part of speech is 8. In Step 1218, RS is set to true.

In Step 1220, mSpellTest in CD_(i) is inspected to determine if at leastone entry in the one-dimensional array is not cleared. That is,mSpellTest is indirectly populated by a transform script line that mayor may not have stated some spelling criteria for the stream. A clearmSpellTest would be one that resulted from a transform script line whereno spelling criteria would have been specified for the stream. Step 1222sets RS to false. Step 1224 verifies if mSpelling in WRD is allowedprovided all mSpellTest in CD_(i). An entry in mSpellTest in CD_(i) maystart or end with the Partial spelling match identifier. Should that bethe case, mSpelling in WRD is only required to have a partial match withthe mSpellTest spelling in CD_(i) (as an example, a mspelling in WRDlike “running” would match a mSpellTest in CD_(i) which contains“_ing”). Step 1226 sets RS to true.

In Step 1228, the mCondBool CB_(i) entry of mPermutationLst in LIcorresponding to the same index as CD_(i) is set to RS. Step 1230changes CD_(i) to be CD_(i+1). Step 1210 is re-invoked which inspectsCD_(i) until it escapes the loop when CD_(i) is CD_(N) at Step 1228.

Once CD_(1−N) were processed from Step 1208 through Step 1230, Step 1210detects that there are no current CD_(i)—since CD_(i) is CD_(N+1)—andStep 1232 is invoked. In Step 1232, the one-dimensional array of stringmWorkPerm of mPermutationLst in LI is copied to a new one-dimensionalarray of string variable named Work Item WI. Strings of the format‘CX.CY.CZ’ are contained in mWorkPerm where X, Y and Z are numberscorresponding to the index in mCondition to be respected in order toform a full sequence. Step 1234 sets the first TCondition mCondition CD₁of mPermutationLst in LI to be the current TCondition CD_(i). Step 1236sets PS and FS to false.

Step 1238 verifies if CD_(i) is before CD_(N+1). Step 1240 inspects theelement CB_(i)—at the same index as the current TCondition CD_(i)—ofmPermutationsLst in LI to determine if it is set to true. If true, Step1242 replaces all strings in WI that starts with ‘Ci . . . ’ with ‘Pi .. . ’ (as an example, the string ‘C1.C3.C4’ would be changed to‘P1.C3.C4’ if i was one). Step 1244 sets mCondRes entry index i CR_(i)of mPermutationLst in LI to WRD.

If CB_(i) is not set to true at Step 1240, at Step 1246, all strings inWI that starts with ‘Ci . . . ’ are replaced with ‘Fi . . . ’ (as anexample, the string ‘C1.C3.C4’ would be changed to ‘F1.C3.C4’ if i wasone).

In Step 1248, CD_(i) in mPermutationLst in LI is changed to becomeCD_(i+1) in mPermutationLst in LI. Step 1238 is then re-invoked untilCD_(i) is CD_(N+1).

In Step 1250, all strings in WI that starts with ‘Fi’ are removed fromthe one-dimensional array of string. Step 1252 inspects each element ofWI to determine if at least one of them is ‘Pi’. Step 1254 sets FS totrue. Step 1456 inspects if mCallback in SPT is clear—i.e. is there acall-back associated with the script. Step 1258 calls the sub-processPermutation Callback with the variables LI, mCallback in SPT, PT and WL.Step 1260 calls the Permutation Callback sub-process at Step 1370 inFIG. 13. Step 1262 sets FS to CBR set by the Permutation Callbacksub-process.

In Step 1264, all strings in WI are inspected to determine if at leastone of them starts with ‘Pi’ without being ‘Pi’—i.e. one of them needsto start with ‘Pi’ (like ‘P2.P3’ for i that is 2) but is not limited to‘Pi’ (like ‘P2’ for i that is 2). Step 1266 sets PS to true. Step 1268copies WI to mWorkPerm of mPermutationLst in LI.

Step 1270 resumes the process following Step 1124 in FIG. 11.

FIG. 13 depicts a flow scheme for a Time Producer Permutation Callbacksub-process as well as a permutation call-back sub-process in thepreferred embodiment of the invention. The Time Producer PermutationCallback sub-process is invoked as a result of a successfulidentification of sequences of TReco from the script in FIG. 9C. Theprogramming engineer can utilize any routine now known or laterdeveloped to validate stream sequences which describe a time that mayhave been uttered. FIG. 13 describes the preferred method related totime sequence validation. The Time Producer Permutation Callback setsNew Stream NS to a stream that contains the time, or clears NS if notime may be constructed from the sequence. The Permutation Callbacksub-process is responsible for adding NS to WL if NS is not cleared.

In Box 1302, the Time Producer Permutation Callback sub-process iscalled from Step 1372 in FIG. 13.

In Step 1304, NS is set to a new TReco. In Step 1306, Hour HR, Minute MNand Second SC number variables are all set to zero.

In Step 1308, mLineName in LI is inspected to determine if it is equalto “TRANSFORMATION”. If yes, Step 1310 sets the variable Stream SM tothe TReco in LN that holds the tag “<WORD>”. In order to detect a tag ina TScptLine, the mCondition array in mPermutationLst is inspected oneelement at a time until a TCondition is detected where mTagNamecorresponds to the tag that is being looked for. When that tag issuccessfully detected, the corresponding entry in mCondRes to the entryin mCondition in mPermutationLst is identified as the TRecocorresponding to the tag. A clear SM identifies that the tag was notdetected in LN. In Step 1312, mSpelling of SM is inspected to determineif it is equal to “noon”. Step 1314 sets HR to 12. If mSpelling in SM isnot equal to “noon”, in Step 1316, mSpelling of SM is inspected todetermine if it is equal to “midnight”. Step 1318 sets HR to 0. Thesub-process proceeds to Step 1364.

If mLineName in LN in Step 1308 is not equal to “TRANSFORMATION”, inStep 1320, mLineName in LI is inspected to determine if it is equal to“TIME FROM AMPM”. If yes, Step 1322 sets the variable Stream SM to theTReco in LN that holds the tag “<HOUR>”. Step 1324 sets HR to thenumerical value of mSpelling of SM. Step 1326 sets the variable StreamSM to the TReco in LN that holds the tag “<MINUTES>”. In Step 1328, SMis inspected to determine if it is cleared. If SM is not cleared, Step1330 sets MN to the numerical value of mSpelling in SM. Step 1332 setsthe variable Stream SM to the TReco in LN that holds the tag “<AMPM>”.

If SM is cleared at Step 1328, the sub-process proceeds to Step 1332. InStep 1334, SM is inspected to determine if it is cleared. If SM is notcleared, in Step 1336, mSpelling of SM is inspected to determine if itis equal to “pm”. Step 1338 adds 12 to HR and the sub-process proceedsto Step 1364. If SM is cleared at Step 1334, the sub-process proceeds toStep 1364.

If mLineName is not equal to “TIME FROM AM/PM” in Step 1320, in Step1340, mLineName in LI is inspected to determine if it is equal to “TIMEFROM OCLOCK”. If yes, Step 1342 sets the variable Stream SM to the TRecoin LN that holds the tag “<HOUR>”. Step 1344 sets HR to the numericalvalue of mSpelling of SM. The sub-process then proceeds to Step 1364.

If at Step 1340 the mLineName is not equal to “TIME FROM OCLOCK”, inStep 1346, mLineName in LI is inspected to determine if it is equal to“TIME FROM DIFF”. If yes, Step 1348 sets the variable Stream SM to theTReco in LN that holds the tag “<TIME>”. Step 1350 extracts HR, MN andSC from mSpelling of SM. Since the TReco in LN that holds the tag“<TIME>” is a TIME part of speech, the spelling is always HR:MN:SC asbuilt from Step 1364. It is then possible to predictably extract HR, MNand SC from mSpelling. Step 1352 sets the variable Stream SM to theTReco in LN that holds the tag “<MINUTES>”. In Step 1354, SM isinspected to determine if it is cleared. If no, Step 1356 sets MN to 60minus the numerical value of mSpelling in SM. Step 1358 decreases HR byone. Step 1360 inspects HR to determine if it is smaller than 0. In Step1362, HR is added 24. The sub-process proceeds to Step 1364. If at Step1354 SM is cleared, the sub-process proceeds to Step 1364.

If mLineName in LN is not equal to “TIME FROM DIFF” in Step 1346, thesub-process resumes following Step 1372 in FIG. 13 at Step 1368.

In Step 1364, mSpelling of NS is set to “HR:MN:SC”. Step 1366 setsmPartOfSpeech of NS to TIME. Step 1368 resumes the process followingStep 1372 in FIG. 13.

In Step 1370, the Permutation Callback sub-process is called from Step1260 in FIG. 12. The Permutation Callback sub-process calls CB and addsNS to WL only if CB did set a value to CB (CB is not clear). ThePermutation Callback sub-process will set CBR to true if a stream wasadded to WL, false otherwise.

The TProc CB variable is a sub-process reference. In the preferredembodiment of the invention, there are two possible values for it: TimeProducer Permutation Callback at Step 1302 in FIG. 13 or Number ProducerPermutation Callback at Step 1402 in FIG. 14. The programming engineeris free to use any other sub-process reference or not use the ones fromthe preferred embodiment of the invention.

In Step 1372, CB is called. CB is required to set or clear the TRecostructure New Stream NS. Step 1374 sets CBR to false. Step 1376 inspectsNS to determine if it is cleared. Should NS be cleared at Step 1376,Step 1378 resumes the process following Step 1260 in FIG. 12.

If NS is not cleared at Step 1376, in Step 1380, mFromTime in NS is setto mFromTime in the first TReco of PT. Step 1382 sets mToTime in NS tomToTime in the last TReco of PT. Step 1384 sets mChildren in NS to PT.Step 1386 sets mStream in NS to concatenated mStream of all TReco in PTfrom the first TReco to the last one. Step 1388 adds NS to WL. Step 1390sets CBR to true. Step 1392 resumes the process following Step 1260 inFIG. 12.

FIG. 14 depicts a flow scheme for a Number Producer Permutation Callbacksub-process in the preferred embodiment of the invention. Thesub-process is invoked as a result of a successful identification ofsequences of TReco from the script in FIG. 9B. The programming engineercan utilize any routine now known or later developed to validate streamsequences which describe a number that may have been uttered. FIG. 14describes the preferred method related to number sequence validation.The Number Producer Permutation Callback sets New Stream NS to a streamthat contains the number, or clears NS if no number may be constructedfrom the sequence. The Permutation Callback sub-process is responsiblefor adding NS to WL if NS is not cleared.

In Box 1402, the sub-process Number Producer Permutation Callback iscalled from Step 1372 in FIG. 13.

In Step 1404, NS is set to a new TReco. Step 1406 determines ifmLineName in LN is “NUMBER TRANSFORM”. If yes, Step 1408, sets a StreamSM to the TReco that holds the tag “<NUMBER>” in LN.

In Step 1410, SM is inspected to determine if it is clear. A clear SMidentifies that the tag was not detected in LN. Step 1412 sets mSpellingof NS to the mExtra content corresponding to the part of speechCARDINAL_NUMBER. For the spelled word “twelve”, the mExtra elementcorresponding to CARDINAL_NUMBER is expected to be “12”. The sub-processproceeds to Step 1414. If at Step 1410 SM is cleared, the sub-processproceeds directly to Step 1414.

In Step 1414, mLineName in LN is inspected to determine if it is “NUMBERCONSTRUCTION”. If Step 1414 fails to identify mLineName in LN as “NUMBERCONSTRUCTION”, Step 1422 resumes the process at Step 1372 in FIG. 13.

If mLineName in LN is equal to “NUMBER CONSTRUCTION” at Step 1414, inStep 1416, Left Stream LS is set to the TReco in LN that holds the tag“<LEFT>”. In Step 1418, Right Stream RS is set to the TReco in LN thatholds the tag “<RIGHT>”.

In Step 1420, LS and RS are inspected to determine if they are both notclear values. If either of LS or RS at Step 1420 is clear, Step 1422resumes the process following Step 1372 in FIG. 13.

In Step 1424, Right Number RN is set to the numerical value of mSpellingin RS. Note that if RN is zero at Step 1424, RN is set to the numericalvalue of mExtra in RS. In Step 1426, Left Number LN is set to thenumerical value of mSpelling in LS. Note that if LN is zero at Step1426, LN is set to the numerical value of InExtra in LS.

If either of LS and RS are clear at Step 1420, in Step 1428, thesub-process determines if LN is a greater number than RN. This wouldidentify sequences of the additive type. For example, Step 1428 succeedsfor sequences of the type “twenty five” (since 20 is greater than 5). IfLN is greater then RN, In Step 1430, the sub-process verifies that thestring built from LN has a greater length than the length of the stringbuilt from RN. For example, Step 1430 fails for sequences of the type“twenty fifteen” (The string “20” has a greater length than the string“5”, but not the string “15”).

If yes at Step 1430, the sub-process next ascertains the order ofmagnitude of the number in terms of the power of ten. In Step 1432, LNis inspected to determine if it is greater or equal to 100000, 100000,1000, 100 or 10. Should LN be 1000, Tens TS would be set to 1000 at Step1432. Step 1434 then sets TS to the corresponding value depending if LNis greater or equal to each tested value in Step 1432. Should LN be 15,TS is set to 10 at Step 1434. If LN is not greater or equal to any ofthese values, TS is set to 1 at Step 1436.

Once the order of magnitude of the number has been determined, in Step1438, LN/TS is inspected to determine if the remainder is zero.Sequences like “fifteen two” would fail at Step 1438 since 15/10 doesnot generate a remainder of zero but a remainder of five. Obtaining aremainder of zero is a mandatory condition to fulfill for a validsequence of numbers of the additive type.

If yes at Step 1438, in Step 1440, the global variable Reprocess is setto true. Reprocess global variable will be later inspected in FIG. 10(Step 1016) to determine if a TScptLine is recursive. Step 1442, setsmPartOfSpeech of NS to mPartOfSpeech of RS. If the sequence analyzedwould be “fifty third”, the TReco structure holding “third” would have amPartOfSpeech that is ORDINAL_NUMBER, the sequence “fifty third” wouldthen also be ORDINAL_NUMBER. Step 1444 sets mSpelling of NS to thestring value of the number generated by LN+RN. The sub-process resumesfollowing Step 1372 in FIG. 13.

If at Step 1438 the remainder from the division of LN/TS is not equal tozero, meaning that sequence is not of the additive type, in Step 1446,it is determined if LN is smaller than RN. The purpose of the followingsteps is to handle sequences of the type “fifteen thousand”. Thosesequences are named the multiplicative type.

Step 1448 determines if the string of LN is smaller than the string ofRN. Sequences of the type “fifteen ten” fail at Step 1448 (since thestring “15” is not smaller than the string “10”). Step 1452 tests RN todetermine if it is 100, 1000, 1000000 or 1000000000. Step 1450 setsmPartOfSpeech of NS to mPartOfSpeech of RS. In Step 1454, mSpelling ofNS is set to the string value of LN*RN. Step 1456 resumes the processfollowing Step 1372 in FIG. 13. If at Step 1452 RN is not equal to 100,1000, 1000000 or 1000000000, the sub-process resumes following Step 1372in FIG. 13 at Step 1456.

FIG. 15 depicts a flow scheme for script file reading sub-processes inthe preferred embodiment of the invention.

In Box 1502, a Process Script Line sub-process is called from Step 1582in FIG. 15. The Process Script Line sub-process processes the stringcontained in Script Line SL, which is typically a single line from atransform script file, and populates the TScptLine structure Line LNaccordingly with processed characters from SL so that LN ends upcontaining all information related to permutations described in SL.

In Step 1504, mPOSTransform in LN is set to POS passed to thesub-process. POS may be UNKNOWN, in which case there would not be anyautomatic transformation associated with SL. An automatic transformationtransform script line is a transform script line that specifies a partof speech after the optional Affectation identifier characters asexplained in FIG. 9. That signals to the algorithm that an automatictransformation should occur without the requirement for a call-backsub-process to be invoked.

In Step 1506, mLineName in LI is set to LN. LN may be clear, in whichcase there would not be any line name associated with LI.

In Step 1508, the logical value mRecursive in LI is set to false. Step1510 sets a new pointer variable Current Char CC to point to the firstcharacter of SL. Step 1512 clears the first mPermutation ofmPermutationLst in LI. The sub-process then invokes Step 1514.

In Step 1514, CC is inspected to determine if it is pointing before theend of SL. Step 1516 determines if CC is pointing to an Openingconditional sequence identifier. If CC is not pointing to an Openingconditional sequence identifier at Step 1516, Step 1518 sets the firstmPermutation of mPermutationLst in LI to be the current mPermutation.Step 1520 determines if the current mPermutation is cleared. A clearedTPermute structure, as in the current mPermutation, is a TPermutestructure that was not populated by any process prior. If it is notcleared, Step 1524 concatenates the character pointed by CC at the endof the current mPermutation. Step 1526 sets the following mPermutationfrom the current mPermutation the current mPermutation. Step 1520 isrepeated until it detects a mPermutation that is cleared. Once itdetermines that a mPermutation is cleared, Step 1522 sets CC to point tothe next character after where it was pointing and Step 1514 is thenre-invoked.

In Step 1516, if CC is pointing to an Opening conditional sequenceidentifier, Step 28 sets the pointer Condition Stop CS to the firstoccurrence of a Closing conditional sequence identifier after CC. InStep 1530, Condition CDN is set to the string that is formed from thefollowing character of CC up to the preceding character of CS.

In Step 1532, the last mPermutation that is not cleared ofmPermutationLst in LI is set to the current mPermutation. Step 1534declares a new string named Permutation PM that holds the same contentas the current mPermutation. In Step 1536, the content of CDN isappended at the end of PM. Step 1538 adds an entry to mPermutation arrayof mPermutationLst in LI with the content PM.

In Step 1540, it is determined if the current mPermutation is the firstmPermutation of mPermutationLst in LI. If it is not the firstmPermutation, then Step 1542 sets the current mPermutation to theprevious mPermutation of mPermutationLst in LI and then re-invokes Step1534. Step 1540 is re-invoked until it gets to the first mPermutation inmPermutationLst in LI. Step 1544 then sets CC to point to the characterafter where CS points and Step 1514 is reprocessed until it determinesthat CC is not before the end of SL anymore. Step 1546 calls thesub-process Finalize Script Line at Step 1152 in FIG. 11. At Step 1548,the process resumes at Step 1582 in FIG. 15.

In Step 1550, a Load Script File sub-process is called from Step 1040,1044, 1048 or 1052 in FIG. 10. The Load Script File sub-processdescribes the loading in memory and filling of a single TScriptstructure provided a given file Script File SF which contains atransform script that respects syntax as stated in FIG. 9. A transformscript may be loaded through other means including, but not limited to,accessing memory range that contains the transform script or obtainingthe transform script accessible from system resources.

In Step 1552, the file SF is opened. Step 1554 clears the TScript SC.Step 1556 sets mCallback in SC to Callback CB passed to the sub-process.The value of CB may be cleared identifying that no call-back isexpected. A cleared CB value is a value that was never set by anyprocess prior to its inspection or that was cleared prior in theprocess. In Step 1558, it is determined if there are more characters toprocess from the reading of the file SF. If there are more character toprocess, then Step 1560 reads one line from SF and sets the line contentto the string Script Line SL. Step 1562 clears Line Name LN. Step 1564determines if there is a Comment identifier character in SL. If there isa Comment identifier character in SL, Step 1566 sets SL up to thecharacter before the Comment identifier character. Step 1566 removes allspaces that are not between Spelling identifiers and all tabs from SL.

In Step 1570, SL is inspected to determine if there is a character Linename separator in it. If there is a Line name separator character in SL,Step 1572 sets LN to the string that goes from the beginning of SL up tothe character before the Line name separator character in SL. In Step1574, SL is set to begin from the character after the Line nameseparator character in SL. Step 1576 is then invoked.

If there are no Line name separator character in SL at Step 1570, Step1576 is invoked. In Step 1576, SL is inspected to determine if there isa sequence of characters forming the Affectation identifier. If there isno Affectation identifier in SL, Part of Speech POS is cleared at Step1584. If there is a sequence forming the Affectation identifier, Step1578 sets POS to the part of speech associated with the spellingfollowing the Affectation identifier in SL. If the POS is not apre-programmed value, the POS is added to the collection of POS. Step1580 terminates SL to the character before the Affectation identifier inSL.

In Step 1582, the sub-process Process Script Line at Step 1502 in FIG.15 is called. Step 1586 adds LI set by the sub-process at Step 1582 tothe first cleared entry of mLine in SC. Step 1558 is then re-invokeduntil it is determined that there are no more character to process fromSF. Step 1588 closes SF, and adds SC to Scripts List and Step 1590resumes the process following Step 1040, 1044, 1048 or 1052 in FIG. 10,depending on which step called the Load Script File sub-process at Step1550 in FIG. 15.

FIG. 16 depicts a flow scheme for script file structures andsub-processes in the preferred embodiment of the invention. FIG. 15 andFIG. 16 describe the sub-processes related to transform script loadinginto memory. Transform script examples can be seen in FIG. 9A, 9B, 9Cand 9D.

In Box 1602, a predetermined and programmed into the system TConditionstructure is defined as an optional mTagName as a string, a mPOSTest asa one-dimensional array of logical values and a mSpellTest as aone-dimensional array of strings. mTagName is optional since theprogramming engineer may not have an associated call-back sub-processassociated with the transform script. Since tags are typically used fromcall-back sub-processes in order to detect a stream within a sequence ofstreams, the fact that no call-back exists for a transform script makesmTagName almost irrelevant. The purpose of a TCondition is to hold allinformation related to criteria parameters for a stream to meet asstated between the opening and closing stream delimiters (as defined inFIG. 9). Stream criteria may be related to part of speech and/orspelling requirements. mPOSTest values entries are related to parts ofspeech criteria. Entry index one in mPOSTest would indicate by a valueof true that part of speech value one is a criteria for the givenTCondition. mSpellTest holds potential spelling related criteria in nogiven order. Any given mSpellTest entry that starts with the Partialspelling match identifier is an end with criteria string match asexplained in FIG. 9.

In Box 1604, a predetermined and programmed into the system TPermutestructure is defined as a mPermutation and mWorkPerm, bothone-dimensional arrays of string, a mCondition one-dimensional array ofTCondition, and mCondRes logical one dimensional array. The purpose of aTPermute is to hold all information related to a transform script lineother than the line name and automatic part of speech transformation.Each mPermutation entry holds a string of the type “C1.C2.C3” whereC_(i) means that condition i as described in i^(th) entry of mConditionneeds to be met. mWorkPerm and mCondRes are later used in scriptexecution.

In Box 1606, a predetermined and programmed into the system TScptLinestructure is defined as an optional mPOSTransform value as a numbercorresponding to the part of speech numeric value or UNKNOWN (which hasan associated numerical value of 0 as explained in FIG. 8) if cleared,followed by an optional m]LineName as a string that refers to a scriptline name if it was found in the read script (as an example,“TRANSFORMATION” is the line name of the first line in the transformscript in 9C), a mRecursive logical value, and, a mPermutationLst thatholds a TPermute structure. The mRecursive logical value is set to trueto signal that a transformation that occurs on that transform scriptline must be followed by a re-interpretation of the same transformscript line. For example, a Stream sequence may be described in atransform script line where any part of speech followed by a NOUN_PHRASEpart of speech generates a NOUN_PHRASE part of speech. A successfulgeneration of a NOUN_PHRASE through that transform script line wouldmean that a new NOUN_PHRASE stream has been created. But that newlycreated NOUN_PHRASE stream would not have been taken into considerationfor that same transform script line if the algorithm would proceedimmediately to the next transform script line. Consequently, thetransform script line is re-evaluated after a successful transform inorder not to miss any streams for analysis to see if they can beincluded in a sequence of streams related to a transform script line,regardless if they were created from the same transform script line. TheTPermute structure holds all information extracted from a singletransform script line. Should mPOSTransform not be UNKNOWN part ofspeech, the transform script line is an automatic transform script linesince it does not require a call to the call-back sub-process for thetransformation to occur. Such transform script lines are the ones thatinclude an Affectation identifier followed by a part of speech. IfmPOSTransform is UNKNOWN part of speech, a call-back associated to theentire transform script should be invoked—where the decision can be madeto allow the sequence of streams to be formed or not.

In Box 1608, a predetermined and programmed into the system TScriptstructure is defined as a one-dimensional array of TScptLine structuresand a mCallback optional value as a TProc that is the address of asub-process to call upon running the script. The purpose of a TScriptstructure is to hold all information related to a transform script. Thatinformation is a simple ordered array of TScptLine (each TScptLine holdsall information related to a single transform script line) and anoptional mCallback value.

In Box 1610, the sub-process Get Condition Entry is called from Step1672 in FIG. 16 or Step 2110 in FIG. 21. The purpose of Get ConditionEntry sub-process is to fill a single TCondition structure in theTPermute structure and sets Condition Entry CE to the index of the addedTCondition. Condition CDN must have been set with the condition stringto create a TCondition in LN prior to calling the Get Condition Entrysub-process. Current Character CHC will scan CDN one character at a timewhile reacting adequately on determined characters related to the syntaxof transform scripts to build successfully the TCondition structure inLN.

In Step 1612, a newly created New Condition NC variable of typeTCondition is cleared. Step 1614 sets the first character of thecondition CDN created at Step 1668 to the Current Character CHc. Step1616 sets a newly created logical variable Look for Tag LFT to the valuetrue. In Step 1618, a newly created Token TK variable points to thecurrent character.

In Step 1620, it is determined if the CH_(C) is before the end of CDN.If CH_(C) is before the end of CDN at Step 1620, Step 1622 verifies ifCH_(C) is a Tag delimiter character and the value of LFT is true. If thetest at Step 1622 succeeds, the value of mTagName in NC is set to thestring from the first character of CDN up to the character just beforeCH_(C) in Step 1624. In Step 1626, the variable TK is set to point tothe character just after CH_(C).

In Step 162 ^(o), CH_(C) is inspected to determine if it points to theCriteria separator (as explained in FIG. 9) or it is actually pointingat the end of CDN. Step 1630 sets the value of LFT to false. In Step1632, a Token Label TL is set to the string that goes from the characterpointed to by TK up to the character preceding CH_(C). Step 1634 removesthe spaces form each extremities of TL.

In Step 1636, the first character of TL is inspected to determine if itis a Spelling identifier. If it is a Spelling identifier, at Step 1636,Step 1638 sets the first cleared entry of mSpellTest in NC to thecontent of TL that is between Spelling identifiers. If the firstcharacter of TL is not a Spelling identifier at Step 1636, Step 1640identifies the part of speech associated with the content of TL and thensets the entry of mPOSTest in NC to the numerical value of the part ofspeech to true. Step 1642 determines if the part of speech obtained atStep 1640 is the same as mPOSTransform in LN. If that is the case, Step1644 sets the logical value mRecursive in LN to true.

Following Step 1638 or Step 1640, Step 1646 sets CH_(C) to the characterfollowing the current character CH_(C+1). If Step 1628 does not identifya Criteria separator as CH_(C) and CH_(C) is not pointing to the end ofCDN, then Step 1646 is invoked. The process is repeated until Step 1620identifies that CH_(C) is beyond the end of CDN. Once Step 1620 escapesthe loop, Step 1648 adds NC to the first available mCondition entry ofmPermutationLst in LN and sets the number variable CE to the index ofthe added entry in mCondition. In Step 1650, the process resumesfollowing Step 1672 in FIG. 16 or Step 2110 in FIG. 21, depending onwhich step called the sub-process.

In Box 1652, the sub-process Finalize Script Line is called from Step1546 in FIG. 15.

In Step 1654, the current mPermutation P_(C) is set to the firstmPermutation of mPermutationLst in vLine. Step 1650 also sets a newlycreated string variable Permutation String PS to the content of thecurrent mPermutation. Step 1656 determines if there is a Pc.

In Step 1658, a string variable Build String BS is cleared. Step 1660sets the current character CH_(C) to the first character of PS.

In Step 1662, it is determined if CH_(C) is before the end of PS or not.If Step 1664 determines that CH_(C) is before the end of PS, Step 1664verifies if CH_(C) is an Opening stream delimiter. If CH_(C) is not anOpening stream delimiter, the sub-process sets CH_(C) to CH_(C+1), atStep 1666. If CH_(C) is an Opening stream delimiter character at Step964, a new string variable Condition CDN is set to the string that isformed from the character following CH_(C) up to the preceding characterfrom the next Closing stream delimiter after CH_(C) at Step 1668.

Step 1670 sets CH_(C) to the character following the next Closing streamdelimiter after CH_(C). In Step 1672, the sub-process Get ConditionEntry at Step 1610 in FIG. 16 is called.

In Step 1674, a character ‘C’ is appended at the end of BS. The stringvalue of CE returned by Get Condition Entry sub-process at Step 1610 inFIG. 16 is appended to the end of BS as well as a ‘.’. Step 1662 is thenre-invoked until it is determined that CH_(C) is not before the end ofPS. Step 1676 then sets P_(C) to BS. In Step 1678, the P_(C) is set toP_(C+1) and Step 1656 is re-invoked until it is determined that P_(C) isP_(N). Step 1680 resumes the process at Step 1546 in FIG. 15 or Step2110 in FIG. 21.

Conceptual Analysis

FIG. 17 depicts a flow scheme for a Conceptual Analysis process in thepreferred embodiment of the invention. The purpose of the ConceptualAnalysis process is to calculate a normalized conceptual representationthat represents the concept related to the inquiry uttered by thespeaker provided the TRecoLst that contains multiple syntacticpermutations calculated in the Syntactic Analysis process.

By way of example and not intending to limit the invention in anymanner, the Conceptual Analysis process may be based on ConceptualDependency theory (CD) as mostly formulated by Roger. C. Schank. Itsgoal is to normalize concepts by removing all syntax related informationfrom the final conceptual representation. The conceptual representationof two sentences that convey the same idea would then need to beidentical. As an example, “What time is it?” and “What is the time?”would both have the same conceptual representation. By way of exampleand not intending to limit the invention in any manner, a conceptualrepresentation can be represented by a Predicate. A Predicate is astring that has the following format:

-   -   PRIMITIVE (ROLE₁:FILLER₁) . . . (ROLE_(N):FILLER_(N))

Where PRIMITIVE is a keyword of one's choosing within a limited set ofpossible primitives—of one's choosing—and may represent an action aswell as a state, ROLE_(i) is a slot to be filled related to PRIMITIVE,and FILLER_(i) is the value related to ROLE_(i) and PRIMITIVE and may bea Predicate as well as a string or even a variable. By way of exampleand not intending to limit the invention in any manner, a variable canbe a string that is preceded by the characters ‘$+’ and followed by thecharacters ‘+$’. As an example “$+COLOR+$” would represent the “COLOR”variable. In the preferred embodiment of the invention, by way ofexample and not intending to limit the invention in any manner,variables and variable names are kept in two synchronizedone-dimensional arrays of string—first one-dimensional array of stringholding the variable names, and second one-dimensional array of stringholding the variables content. (ROLE_(i):FILLER_(i)) is named arole-filler pair. A Predicate may contain any number of role-fillerpairs greater or equal to one. The order of role-filler pairs within thePredicate is irrelevant. Variables detected in fillers are interpretedas an identification that role-filler pair has a variable filler.Variables used in primitives or roles are variable tokens as describedbelow and result in the value related to variable to replace thevariable token.

Contrary to Schank's theory surrounding the use of primitives, theinvention does not limit itself to the 12 primitives stated by Schank.There are significant debates in the field of conceptual dependencyabout the minimal set of primitives required to describe every flavor ofconceptual representation. The purpose of this invention is not to enterin such debate by limiting the programming engineer to a fixed set ofprimitives; consequently, the programming engineer is free to use theprimitive set he desires to represent knowledge. In the flight responsesystem often referred to in the invention, as an example, theAIRLINEPOSTANALYSIS primitive is used. This is obviously not a primitivethat could be useful to represent knowledge broadly in a context largerthan a flight response system. But, it is extremely useful in thelimited context of such flight response system since it can well beinterpreted as a non-reducible element of knowledge in that context.This is actually helpful since a full reduction to real primitives wouldmean that a flight response system be required to detect a report beingrequested during the Post Analysis process, as an example, from realprimitives like MTRANS. This would be a significant task and a majorbarrier for the programming engineer's efficiency to produce a usefulsolution in a reasonable amount of time.

In order to help understanding, by way of example and not intending tolimit the invention in any manner, some valid conceptual representationsfollow:

-   1. “John gave his car to Paul.”

ATRANS (OBJECT: CAR) (FROM: JOHN) (TO: PAUL) (TIME: PAST)

ATRANS, in conceptual dependency theory, is one of 12 action primitivesused and refers to a transfer of possession—the abstract transfer ofpossession from one person to another, as in a give or a buy. Nophysical transfer need take place; the transfer occurs purely on theplane of ownership.

-   2. “John remembered that he gave his car to Paul.”

MTRANS (ACTOR: JOHN) (MOBJECT: ATRANS (OBJECT: CAR) (FROM: JOHN) (TO:PAUL)

(TIME: PAST)) (FROM: LTM) (TO: CP) (TIME: PAST)

MTRANS, in conceptual dependency theory, is one of 12 action primitivesused and refers to the transmission of an IDEA—some conceptualization istransmitted from one head to another (or within the same head). Tell,forget and remember can all be expressed with MTRANS. An idea isrepresented by an MOBJECT slot in the CD, which is superficially likeOBJECT except that it contains a whole concept as its value.

LTM, in conceptual dependency theory, refers to the location that storesmemory in one's mind.

CP, in conceptual dependency theory, refers to the central processor ofone's mind. A conceptual representation that MTRANS from LTM to CP isthe conceptual representation of remembering something.

As it can be seen in this example, the filler associated with the roleMOBJECT is a complete Predicate structure.

-   3. “A blue car.”

PP (OBJECT: CAR) (COLOR: BLUE)

PP, in conceptual dependency theory as stated by Roger C. Schank and hisfollowers, refers to a picture producer—i.e. anything that can generatea picture in one's mind. In this case, one's mind may easily generate acar picture.

-   4. “A train and a car that are the same color.”

AND (VALUE1: PP (OBJECT: TRAIN) (COLOR: $+COLOR+$)) (VALUE2: PP (OBJECT:CAR) (COLOR: $+COLOR+$))

Without specifying the exact color of neither the car nor the train,this Predicate specifies through the variable $+COLOR+$ that bothobjects need to be the same color.

-   5. “A train and a car that are not the same color.”

AND (VALUE1: PP (OBJECT: TRAIN) (COLOR: $+COLOR1+$)) (VALUE2: PP(OBJECT: CAR) (COLOR: $+COLOR2+$))

Without specifying the exact color of neither the car nor the train,this Predicate specifies through the variable $+COLOR1+$ and $+COLOR2+$that both objects need to be of different colors.

The programming engineer may implement any predicate calculus operationthat he sees fit. Predicate calculus operations are helpful to performpost-analysis and also to assist in the Command Handler. The preferredembodiment of the invention, by way of example and not intending tolimit the invention in any manner, defines some sub-processes related topredicate calculus manipulations:

-   1. The predicate calculus operation P_(X) is a P_(Y): returns true    if P_(X) is a P_(Y), returns false otherwise. As an example, PP    (OBJECT: TRAIN) (COLOR:RED) is a PP (OBJECT: TRAIN) returns true,    i.e. a “red train” is a “train”. On the other hand, PP (OBJECT:    TRAIN) is a PP (OBJECT: TRAIN) (COLOR:RED) returns false since a    “train” is not necessarily a “red train”. Furthermore, PP (OBJECT:    TRAIN) (COLOR:RED) is a PP (OBJECT: TRAIN) (COLOR: $+COLOR+$)    returns true since a “red train” is a “colored trained” and, upon    evaluation of the sub-process, the variable $+COLOR+$ is set to RED.    Note that PP (OBJECT: TRAIN) (COLOR:RED) is a PP (COLOR:RED)    (OBJECT: TRAIN) also returns true since the order of role-filler    pairs in a Predicate structure is irrelevant.-   2. The predicate calculus operation P_(X) has a P_(Y): returns true    if P_(X) is or contains the Predicate P_(Y), returns false    otherwise. As an example, MTRANS (ACTOR: JOHN) (MOBJECT: ATRANS    (OBJECT: CAR) (FROM: JOHN) (TO: PAUL) (TIME: PAST)) (FROM: LTM) (TO:    CP) (TIME: PAST) has a ATRANS (OBJECT: CAR) returns true, i.e. “Does    John remembering that he gave his car to Paul have anything to do    with a car changing possession?” returns true. In the same way as    the is a sub-process, variables can be used. As an example, MTRANS    (ACTOR: JOHN) (MOBJECT: ATRANS (OBJECT: CAR) (FROM: JOHN) (TO: PAUL)    (TIME: PAST)) (FROM: LTM) (TO: CP) (TIME: PAST) has a MTRANS (ACTOR:    $+SOMEONE+$) (MOBJECT: $+SOMETHING+$) (FROM: LTM) (TO: CP) (TIME:    PAST) also returns true and upon evaluation of the sub-process    $+SOMEONE+$ is set to JOHN and $+SOMETHING+$ is set to ATRANS    (OBJECT: CAR) (FROM: JOHN) (TO: PAUL) (TIME: PAST). This example    could be read as the following. “Did someone ($+SOMEONE+$) remember    something ($+SOMETHING+$) in the Predicate MTRANS (ACTOR: JOHN)    (MOBJECT: ATRANS (OBJECT: CAR) (FROM: JOHN) (TO: PAUL) (TIME: PAST))    (FROM: LTM) (TO: CP) (TIME: PAST)?” In Which case the has a    sub-process returns true and $+SOMEONE+$ is set to JOHN,    $+SOMETHING+$ is set to the Predicate ATRANS (OBJECT: CAR) (FROM:    JOHN) (TO: PAUL) (TIME: PAST) meaning “John gave his car to Paul”.-   3. The predicate calculus operation P_(X) replacement of P_(Y) with    F_(Z). This predicate calculus operation replaces P_(Y) in P_(X)    with filler F_(Z) if found. As an example, MTRANS (ACTOR: JOHN)    (MOBJECT: ATRANS (OBJECT: CAR) (FROM: JOHN) (TO: PAUL) (TIME: PAST))    (FROM: LTM) (TO: CP) (TIME: PAST) replacement of MTRANS (ACTOR:    JOHN) with PAUL. That would result in the Predicate MTRANS (ACTOR:    PAUL) (MOBJECT: ATRANS (OBJECT: CAR) (FROM: JOHN) (TO: PAUL) (TIME:    PAST)) (FROM: LTM) (TO: CP) (TIME: PAST). The same way as for the    other predicate calculus operations, variables may be used. The    operation MTRANS (ACTOR: JOHN) (MOBJECT: ATRANS (OBJECT: CAR) (FROM:    JOHN) (TO: PAUL) (TIME: PAST)) (FROM: LTM) (TO: CP) (TIME: PAST)    replacement of MTRANS (ACTOR: $+SOMEONE+$) with PAUL would result in    the same Predicate MTRANS (ACTOR: PAUL) (MOBJECT: ATRANS (OBJECT:    CAR) (FROM: JOHN) (TO: PAUL) (TIME: PAST)) (FROM: LTM) (TO: CP)    (TIME: PAST) and $+SOMEONE+$ would be set to JOHN upon execution of    the sub-process.

The preferred embodiment of the invention, by way of example and notintending to limit the invention in any manner, would, as a minimum,implement the predicate calculus manipulations operations is a, has aand replacement of with. These operations are self explanatory andsimple string manipulations operations that can easily be programmed bythose skilled in the art.

In order to manipulate Predicate structures in the preferred embodimentof the invention, as way of example and not intending to limit theinvention in any manner, a Predicate Builder scripting language is used.The Predicate Builder scripting language is an interpreted language thatperforms simple text replacement operations in order to generate asingle Predicate, i.e. a string that is of the form PRIMITIVE(ROLE₁:FILLER₁). (ROLE_(N):FILLER_(N)). Every token that needs specialprocessing in the Predicate Builder scripting language of the preferredembodiment of the invention is located between some designatedcharacters, here the ‘$+’ and ‘+$’ characters. Other characters that arenot between ‘$+’ and ‘+$’ are simply added to the calculated result. Byway of example and not intending to limit the invention in any manner,categorization of tokens can be as following:

-   1. Variable token: A variable which content replaces the token. or,-   2. Procedural token: A procedure to call where some optional    parameters are passed and the optional result replaces the token.    or,-   3. Entry-point token: An entry-point to the system where some    predetermined and programmed into the system content replaces the    token. or,-   4. Flow-control token: A predetermined and programmed into the    system flow-control token like $+IF( )+$, $+IFNOT( )+$, $+ELSE+$ and    $+ENDIF+$. or,-   5. Definition token: Definition of variable or procedural content    through the tokens $+DEFINE( )+$ or $+EVALDEFINE( )+$.

To help understanding, as way of example and not intending to limit theinvention in any manner, a Predicate Builder script example follows:

$+DEFINE(tmp.qry)+$ {?ENTITY} $+IF($+WORKINGCDPREDICATE+$;NULL)+$$+DEFINE(tmp.qry)+$ {?TIME} $+ENDIF+$ $+DEFINE(tmp.rs)+$ {MOOD(CLASS:$+tmp.rs_1+$) (QUERY:$+tmp.qry+$) (OBJECT:$+SUBJECT+$)}$+tmp.rs(INTEROGATIVE)+$ $+UNDEF(tmp.qry)+$ $+UNDEF(tmp.rs)+$

The first line $+DEFINE(tmp.qry)+$ {?ENTITY} is a definition token. Thecontent between brackets is associated to the variable token$+tmp.qry+$. To keep track of such association, the system keeps twoone-dimensional arrays of string. One of them holds variable names (inthis case “tmp.qry”) and the second one, at the same index in the array,holds corresponding content (in this case “?ENTITY”).

Next, a line follows having a flow-control token and an entry-pointtoken. $+IF($+IF_1+$;$+IF_2+$)+$ is a flow-control token that lets thescript interpret content up to the corresponding $+ELSE+$ or $+ENDIF+$only if $+IF_1+$ is equal to $+IF_2+$. Should $+IF_1+$ not be equal to$+IF_2+$, scripting would start being interpreted after thecorresponding $+ELSE+$ or $+ENDIF+$ depending on the script content. Thetoken $+WORKINGCDPREDICATE+$ is an entry-point token. Just by looking atit, one could not say if it is a variable token or an entry-point token,but implementation of both are different since an entry-point tokenrequires runtime processing in order to generate replacement content anda variable token strictly replaces content.

Next, the line $+DEFINE(tmp.qry)+$ {?TIME} is also a definition token.Note that this line won't be interpreted if the flow-control token$+IF+$ fails on the preceding line to determine that the entry-pointtoken $+WORKINGCDPREDICATE+$ is NULL.

Next, the flow-control token $+ENDIF+$ follows, which corresponds to theprevious flow-control token $+IF+$.

The following line is also a definition token. But, this time thecontent between brackets is associated to the procedural token$+tmp.rs(param1)+$ since the parameter $+tmp.rs_1+$ is referred (statingthat it requires a procedural token to be fully expanded). Allprocedural token may refer to parameters accessible from the variabletoken that is the same as the name of the procedural token appended withthe ‘_’ character and the parameter index. Note that within thedefinition of the procedural token, the entry-point token $+SUBJECT+$ isalso used.

The line $+tmp.result(INTEROGATIVE)+$ is a procedural token. Andfinally, the lines $+UNDEF(tmp.qry)+$ and $+UNDEF(tmp.rs)+$ areentry-point tokens that clears the variables tmp.qry and tmp.rs.

Interpretation of this Predicate Builder script would go as follow(assume that entry-point token $+WORKINGCDPREDICATE+$ returns NULL andthat entry-point token $+SUBJECT+$ is “PP (OBJECT: CAR) (COLOR: RED)”).

-   1. Set $+tmp.qry+$ to “?ENTITY”.-   2. $+IF($+WORKINGCDPREDICATE+$;NULL)+$ falls thru since    $+WORKINGCDPREDICATE+$ returned NULL.-   3. Set $+tmp.qry+$ to “?TIME”.-   4. Set $+tmp.rs+$ to “MOOD (CLASS:$+tmp.rs_1+$) (QUERY:$+tmp.qry+$)    (OBJECT:$+SUBJECT+$)”.-   5. Append to result buffer of Predicate script interpretation the    string “MOOD (CLASS:INTEROGATIVE) (QUERY:?TIME) (OBJECT:PP (OBJECT:    CAR) (COLOR: RED))”. All replacements were then made provided that    state of variable tokens and entry-point tokens at time of    interpretation.-   6. Clear $+tmp.qry+$.-   7. Clear $+tmp.rs+$.

The final result from Predicate Builder script interpretation of thescript is the string “MOOD (CLASS:INTEROGATIVE) (QUERY:?TIME) (OBJECT:PP(OBJECT: CAR) (COLOR: RED))” which respects the format required to forma Predicate structure.

Definitions, flow-controls, variables and procedurals tokens can be usedwithout constraint in Predicate Builder scripts. Entry-Point tokens needto respect requirements related to parameters to passed to it as well asthey need to be used while having a good understanding on the runtimeprocessing corresponding to each token.

Interpreted languages were developed for years and such implementationis well known to those skilled in the art. The Predicate Builderscripting language is another interpreted language that has thespecificity of generating Predicate structures (in this case, a stringthat respects the format earlier stated). The advantages of using thePredicate Builder scripting language over any traditional language suchas C, C++ or else is that it is scalable, opened, dedicated to the taskof generating a Predicate structure and logics related to Predicatebuilding mostly reside outside binary code.

One or many Predicate Builder scripts can be associated to any word—partof speech pair. This relates to the reality that any word may havemultiple meanings, and that meanings do not normally cross the part ofspeech boundary (as examples, the VERB part of speech “fire” is notexpected to have the same meaning as the NOUN part of speech “fire”, andthe NOUN part of speech “ring” that one wears does not have the samefunction or meaning than a boxing “ring”).

A unique Predicate Builder script may also be associated to any word ofa given part of speech. As an example, the CARDINAL_NUMBER orORDINAL_NUMBER parts of speech. Although the invention is not solimited, it would be impractical to require a unique Predicate Builderscript to define the CARDINAL_NUMBER “one” and a different one for “two”and so on. Instead, auto-scripts are used in such situations. Anauto-script is a Predicate Builder script that typically can beassociated with all words of a predefined part of speech. By way ofexample and not intending to limit the invention in any manner, whendesired, auto-scripts are defined by populating a procedural token$+.autoscript&POS+$ where ‘POS’ is the part of speech.

To define an auto-script for CARDINAL_NUMBER parts of speech words, thefollowing syntax would typically be used:

$+DEFINE(.autoscript&CARDINAL_NUMBER)+$ { # Put Predicate Builder scripthere }

For example, in FIG. 9D, the part of speech FLIGHT is defined. Thesequences of words “United airline flight number six hundred”, “Flightsix hundred”, “UAL number six hundred” all generate a FLIGHT part ofspeech. In order to assign a valid Predicate to the FLIGHT part ofspeech, an auto-script Predicate Builder script needs to be associatedwith the part of speech FLIGHT (by defining the procedural token.autoscript&FLIGHT). Content of the Predicate Builder script should inthat case detect an optional airline company name in any child node ofthe stream in the syntactic hierarchy, and should also detect aCARDINAL_NUMBER to identify the flight number. Once those elements areextracted from the stream, a database search can then extract allrelevant information related to the flight specified in the stream forpurposes of response to the inquiry. By way of example and not intendingto limit the invention in any manner, the FLIGHT auto-script couldgenerate the following Predicate for the sequences of words statedpreviously:

PP (CLASS:VEHICLE) (TYPE:AIRPLANE) (COMPANY:UA) (NUMBER:600)(ORIGIN:JFK) (DESTINATION:DFW) (STATUS:ARRIVED) (DEPARTURETIME:8:59)(ARRIVALTIME:14:32) (INITIALDEPARTURETIME:8:52)(INITIALARRIVALTIME:14:20) (DEPARTUREGATE:B 21) (ARRIVALGATE:B 2)

In order to build Predicate structures where all elements related tosyntax are removed, it is a bit of a contradiction, but nevertheless afact that mostly syntactic related operations are required. Already, asit can be seen in Box 2340 of FIG. 23, a hierarchy of syntactic streamsare available for conceptual analysis. In this example of an airlineresponse system, the syntactic organization selected by the programmingengineer for conceptual analysis is the SENTENCE, and so the followingdiscussion refers solely to sentences. However, the invention is not solimited and the programming engineer may designate any syntacticorganization, from any portion of an audio input, for conceptualanalysis. For each SENTENCE to be analyze conceptually, the SetTransient Information sub-process shown in FIG. 23 is called. The SetTransient Information sub-process sets the TTransient structure in eachTReco structure so that they can be related to each other on achild-parent basis as seen in Box 2340 in FIG. 23.

In Step 1702, the Conceptual Analysis process is started according tothe preferred embodiment of the invention. The purpose of the ConceptualAnalysis Process is to calculate the Inquiry Predicate IP thatrepresents the conceptual representation of the inquiry as well as thePost Analysis Predicate PAP that represents the conceptualrepresentation of the response to inquiry. In order to do that, allSENTENCE parts of speech that spans from the beginning to the end of thephoneme stream PS are analyzed until successful IP and PAP arecalculated or until all SENTENCE parts of speech streams were calculatedwithout successfully generating an IP and PAP.

Inquiry anomalies may be derived from utterances. In the preferredembodiment, there are three potential inquiry anomalies expressed.Inquiry anomalies, ranked from less inquiry anomaly to most inquiryanomaly, are no inquiry anomaly, a WARNING Predicate in the inquiryPredicate or the response Predicate, and an ERROR Predicate in theinquiry Predicate or the response Predicate. The invention may includean approach where inquiry anomalies are expressed with other scaledvalues, like numbers, as an example; or the invention may also includean approach where inquiry anomalies are not used. As an example, in anhypothetical flight response system which uses the inquiry anomaliesfrom the preferred embodiment, if a speaker said something like “HasAmerican airline flight six hundred and twenty been delayed?” and thereis no flight 620 in the database of flights, to form the response, anERROR Predicate would be added to PAP with a filler containing a stringexplaining what the error is (something like “I'm sorry, there is noflight six hundred and twenty scheduled for today.”). Following the samelogic, a warning may result from conceptual analysis. The same utterancemay result in a warning if a flight 620 exists, but is operated byUnited Airlines instead of American Airlines. In that case, a WARNINGPredicate is generated and the filler contains “Note that flight 620 isoperated by United Airlines instead of American Airlines as you stated”.

The programming engineer is free to use the inquiry anomaly, includingno inquiry anomaly if desired, that will better serve its purpose. TheWARNING and ERROR roles is the inquiry anomaly mechanism chosen in thepreferred embodiment of the invention and does not pretend to limit theinvention in any manner.

Should a WARNING or ERROR role be detected in a calculated PAP,calculations of subsequent SENTENCE parts of speech streams continueuntil all of them are calculated or one is calculated that has noWARNING or ERROR role. The assumption is made that a speaker is aware ofwhat can be spoken, and that between two potential utterances that couldhave been recognized, the more accurate one is picked—i.e. the one thatgenerated no WARNING and ERROR role wins over the one that generated aWARNING role which wins over one that generated an ERROR role.

Furthermore, Conceptual Analysis or Post Conceptual Analysis may decideto reserve a perfectly good IP or PAP. That is done by invoking the$+RESERVE+$ entry point token from a Predicate Builder script. As anexample, if the sequence “be 4” is detected during Conceptual Analysis,knowing that it is more probably a mistake for “before”, the programmingengineer may immediately flag the current conceptual analysis to be areserve since it may not be a valid analysis, although there is a remoteprobability that it is valid. Should Conceptual Analysis later process asequence of words that is also valid and was not flagged as reserve, itwould then be picked over the sequence that was flagged as reserve.

Step 1704 clears variables Inquiry Predicate IP, Post Analysis PredicatePAP, Error Post Analysis Predicate EPAP and Reserve Inquiry PredicateRIP used in the Conceptual Analysis process.

Step 1706 sets SM to the first TReco in WL. Step 1708 inspectsmPartOfSpeech in SM to determine if it is equal to the SENTENCE part ofspeech value. If yes, Step 1710 inspects mStartStream in SM to determineif it is equal to 0 and mEndOFStream in SM to determine if it is equalto TSC. If no at either Step 1708 or Step 1710, the process proceeds toStep 1752.

If yes at Step 1710, in Step 1712, mParent of mTransient in SM iscleared. Step 1714 invokes the Set Transient Information sub-process inStep 2304 of FIG. 23 so that a syntactic hierarchy, as shown as anexample in Box 2340 of FIG. 23, is calculated. Step 1716 sets ReserveRSV and Reject RCT to false. Step 1718 clears the Predicate Subject SBJ,the Predicate Report Subject RSBJ, the Predicate Working Predicate WPREDand the stream Current Packet CP. Step 1720 sets Subject Search SS tofalse.

Step 1722 calls the sub-process Calculate Predicate for Stream at Step1802 in FIG. 18. The Calculate Predicate for Stream sets the Predicatevalue Result Predicate RP accordingly. Step 1724 inspects RP todetermine if it is clear. If yes, the process proceeds to Step 1750.

If no at Step 1724, Step 1726 performs the Post Analysis process onResult Predicate and generates a Predicate Post Analysis ResultPredicate PARP corresponding to the response of RP.

In the preferred embodiment of the invention, the Post Analysis processcomprises the selection of concepts to transform from inquiry toresponse as one or more Predicate structures, defined by the programmingengineer, that may be part of the inquiry uttered expressed as theResult Predicate RP structure. This Post Analysis process results in anew Predicate structure being generated in Post Analysis ResponsePredicate PARP which holds the response to the inquiry to be executed bythe Command Handler if selected.

In the airline response system examples of this application—available inthe examples section of this application, the programming engineerproduced Predicate Builder scripts associated with each word that may beused to utter a command so that a successfully built inquiry Predicateholds at least one Predicate with the primitive AIRLINEPOSTANALYSIS andthe role OPERATION. The filler associated with the OPERATION role, as aconsequence of the programming engineer's choice, is another PredicateREPORT (VALUE:$+VALUETOREPORT+$) (OBJECT:$+OBJECT+$), VERIFY(ORIGIN:$+CITY+$) (OBJECT:$+OBJECT+$) or VERIFY (DESTINATION:$+CITY+$)(OBJECT:$+OBJECT+$).

Following choices from the programming engineer, both Predicates withthe primitive VERIFY are used in order to verify that the origin ordestination of a flight, described in $+OBJECT+$, is indeed $+CITY+$,$+VALUETOREPORT+$ in the Predicate with the primitive REPORT may haveany of the following values and is associated the concept related toinquiry that follows.

-   STATUSARRIVED: Has the flight arrived?-   ARRIVALTIME: What is the arrival time?-   ARRIVALCITY: What is the arrival city?-   ARRIVALGATE: What is the arrival gate?-   ARRIVALLOCATION: What is the arrival location?-   ARRIVALDELTASTATUS: How late or how early is the flight?-   TIMETOARRIVAL: How much time is left to the flight's arrival?-   STATUSDEPARTED: Has the flight departed?-   DEPARTURETIME: What is the flight's departure time?-   DEPARTURECITY: What is the flight's departure city?-   DEPARTUREGATE: What is the flight's departure gate?-   DEPARTURELOCATION: What is the flight's departure location?-   DEPARTUREDELTASTATUS: How early or how late is the flight departure?-   TIMETODEPARTURE: How much time is left until a flight's departure?-   DELTASTATUS: How much time until the departure or arrival of a    flight?-   STATUS: What is the flight's status?

The Post Analysis process task, as an example in the flight responsesystem, is limited at investigating every Predicate within RP with theAIRLINEPOSTANALYSIS primitive and populate the Predicate PAPR so that itholds the response to produce.

The Post Analysis process is then tightly related to the programmingengineer's choices made during Predicate Builder script production. Theprogramming engineer may choose to handle identified concepts while nothandling others, and Predicate construction that happens during theConceptual Analysis process only supports the programming engineer'schoices in the sense that Predicate will be built following the rulesthat were set. The Post Analysis process is as diverse as there arepurposes for this invention. Also, there is not only one way to handle aspecific implementation of this system for the programming engineer.Another implementation of a flight response system could well have useddifferent assumptions during the Predicate Builder script productionphase, that would have resulted in a different Post-Analysis process andwould be equally valid, although different, as the implementation shownas example in this application.

Step 1728 inspects PARP to determine if it is clear. If yes, the processproceeds to Step 1766. If no, Step 1730 inspects RCT to determine if itis true. If yes, the process proceeds to Step 1766.

If no, Step 1732 inspects PARP to determine if it is Has a WARNING(CONTENT: $+CONTENT+$) Predicate. As explained earlier, the Has a is apredicate calculus operation that returns true if the Predicate is foundanywhere in PARP. If yes, Step 1734 inspects EPAP to determine if it isclear. If yes, the process proceeds to Step 1738. If no, Step 1736inspects EPAP to determine if it Has a WARNING (CONTENT: $+CONTENT+$)Predicate. If no, Step 1738 sets EPAP to PARP and RIP to RP and theprocess proceeds to Step 1766. If yes, the process proceeds to Step1766.

If no at Step 1732, Step 1740 inspects PARP to determine if it Has aERROR (CONTENT: $+CONTENT+$) Predicate. If yes, the process proceeds toStep 1750. If yes, the process proceeds to Step 1738. If no, the processproceeds to Step 1766.

If no at Step 1740, Step 1742 inspects RSV to determine if it is true.RSV may have been set to true by an entry point token in any of thePredicate Builder script interpreted from Step 1814 in FIG. 18. If no,Step 1744 sets IP to RP and PAP to PARP and the process proceeds to Step1766. If no, the process proceeds to Step 1746.

Step 1746 inspects RPAP to determine if it is clear. If yes, Step 1748clears EPAP, sets RPAP to PARP, and sets RIP to RP. If no, the processproceeds to Step 1766.

Step 1750 inspects EPAP to determine if it is clear.

If no at either of Step 1708 or Step 1710, Step 1752 inspects SM todetermine if it is the last TReco in WL and inspects IP to determine ifit is not clear. If no, the process proceeds to Step 1766.

If yes at Step 1752, Step 1754 inspects PAP to determine if it is clear.If no, the process proceeds to Step 1760. If yes, Step 1756 inspectsRPAP to determine if it is clear. If no, Step 1758 sets PAP to RPAP andIP to RIP and the process proceeds to Step 1760. If yes at Step 1756,Step 1762 inspects EPAP to determine if it is clear. If no, Step 1764sets PAP to EPAP and IP to RIP and the process proceeds to Step 1760. Ifyes, the process proceeds to Step 1768.

Step 1760 executes PAP. A Predicate can indeed be executed since aPredicate may hold some action primitives that may be interpreted asoperations to execute. As an example, if PAP Has a SPEAK (CONTENT:$+CONTENT+$) Predicate, $+CONTENT+$ shall be spoken back to the userthrough a synthesized voice.

Step 1768 stops the Conceptual Analysis process.

FIG. 18 depicts a flow scheme for a Calculate Predicatefor StreamSub-process and a Calculate Predicatefor Children sub-process in thepreferred embodiment of the invention.

The Calculate Predicatefor Stream sub-process calculates the Predicatefor SM which may have any given mPartOfSpeech value. Upon callingCalculate Predicatefor Stream sub-process, the value of WPRED isimportant since WPRED contains the working Predicate that is beingincrementally built from the Calculate Predicate for Stream sub-process(it is a potentially recursive sub-process). Once the Predicate wascalculated for SM, it sets Result Predicate RP to contain the Predicatecalculated.

In Box 1802, the Calculate Predicatefor Stream sub-process is calledfrom Step 2038 in FIG. 20, Step 1936 in FIG. 19, Step 1848 in FIG. 18 orStep 1722 in FIG. 17. Step 1804 sets Has Rule HR to false. Step 1806inspects mRecoType in SM to determine if it is equal to WORD_ENTRY. Ifno, the process proceeds to Step 1818. If yes, Step 1808 inspects ifthere is a mCDScript entry that is not clear in SM. Since the FlattenScript sub-process was called at FIG. 7 prior to adding the TReco to WL,the algorithm can count on the fact that at most, one Predicate Builderscript will be in mCDScript in SM. If yes, Step 1810 sets CDScript CD tothe first mCDScript that is not clear in SM. Step 1812 sets CP to SM,and Step 1814 parses CD. Parsing involves applying the stringreplacements related to a Predicate Builder script in such a way thatall tokens were processed and that result is a Predicate. Once thePredicate was calculated, it is automatically put in WPRED at Step 1816and the process proceeds to Step 1832.

In Step 1816, Result Predicate RP is set to WPRED. If Step 1806 or Step1808 fails, Step 1818 inspects mPartOfSpeech in SM to determine if it isSENTENCE. If yes, Step 1820 calls the Calculate Predicatefor SENTENCEStream sub-process at Step 2002 in FIG. 20 and the process proceeds toStep 1826. If no, Step 1822 inspects mPartOfSpeech in SM to determine ifit is NOUN_PHRASE. If yes, Step 1824 calls the Calculate PredicateforNOUN_PHRASE Stream sub-process at Step 1902 in FIG. 19. If no, theprocess proceeds to Step 1826. Step 1826 inspects HR to determine if itis true. HR may have been set to true in Calculate Predicatefor SENTENCEStream or Calculate Predicatefor NOUN_PHRASE Stream sub-process.

If no, Step 1828 sets Stream to Calculate STC to SM. Step 1830 calls thesub-process Calculate Predicate for Children at Step 1834 in FIG. 18.The sub-process then moves to Step 1816 where RP is set to WPRED and theprocess proceeds to Step 1832. If yes at Step 1826, the process proceedsto Step 1832. At Step 1832, the process resumes following Step 2038 inFIG. 20, Step 1936 in FIG. 19, Step 1848 in FIG. 18 or Step 1722 in FIG.17.

In Box 1834, the sub-process Calculate Predicate for Children may becalled from Step 2066 in FIG. 20, Step 1942 in FIG. 19 or Step 1830 inFIG. 18. The sub-process will calculate the Predicate of STC that wouldhave been set by the caller and put the result in WPRED before resumingthe process at the caller's position.

Step 1836 sets Keep Stream to Calculate KSTC to STC. Step 1838 sets STCto the first mChildren in STC and clears WPRED. Step 1840 inspects STCto determine if it is clear. If no, Step 1842 inspects mSubject ofmTransient in STC to determine if it is true. If no, Keep Stream KS isset to SM at Step 1844. Step 1846 sets SM to STC. Step 1848 calls thesub-process Calculate Predicatefor Stream at Step 1802 in FIG. 18. Step1850 sets SM to KS. Step 1852 sets WPRED to RP calculated at Step 1848and the process proceeds to Step 1854. If yes at Step 1842, the processproceeds to Step 1854.

Step 1854 sets STC to the following mChildren of mParent of mTransientin STC and Steps 1840 to 1854 are repeated until all mChildren wereprocessed, at which point Step 1840 will succeed. If Step 1840 succeeds,Step 1856 sets STC to KSTC. Step 1858 resumes the process following Step2066 in FIG. 20, Step 1942 in FIG. 19 or Step 1830 in FIG. 18, dependingon which step called the sub-process.

FIG. 19 depicts a flow scheme for a Calculate Predicate for NOUN_PHRASEStream sub-process in the preferred embodiment of the invention.

The sub-process assumes that SM is set to the TReco which stream needsto be calculated. It also assumes that SM has a mPartOfSPeech value ofNOUN_PHRASE. Upon completion, the sub-process will have set ResultPredicate RP to the Predicate holding the conceptual representation ofSM.

In Step 1902, the Calculate Predicate for NOUN_PHRASE Stream sub-processis called from Step 1824 in FIG. 18. Step 1904 sets Direction DIR toSAMENODE. Step 1906 sets Depth DPT to SAMELEVELORLOWER. Step 1908 setsPart of Speech POSS to “GERUNDIVE_PHRASE”. Step 1910 calls the FindPacket sub-process at Step 2102 in FIG. 21.

Step 1912 inspects Find Packet Result FPR, which could have been set inFind Packet sub-process, to determine if it is clear. If no, the processproceeds to Step 1954. If yes, Step 1914 sets POSS to “REL_CLAUSE” andStep 1916 calls the Find Packet sub-process at Step 2102 in FIG. 21.Step 1918 inspects FPR to determine if it is clear. If no, the processproceeds to Step 1954. If yes, POSS is set to“NOUN|PLURAL|PROPER_NOUN|TIME |DATE|PRONOUN” at Step 1920 and Step 1922calls the Find Packet sub-process at Step 2102 in FIG. 21.

Step 1924 inspects FPR to determine if it is clear. If yes, the processproceeds to Step 1954. If no, HR is set to true and SM is set to FPR atStep 1926. Step 1928 inspects SBJ to determine if it is clear. If yes,Put in Subject PiS is set to true at Step 1930 and the process proceedsto Step 1934. If no, the process proceeds to Step 1932 where PiS is setto false, and the process proceeds to Step 1934.

Step 1934 sets Keep Working Predicate KWPRED to WPRED. Step 1936 callsCalculate Predicate for Stream at Step 1802 in FIG. 18. Step 1938 setsWPRED to RP. Step 1940 sets STC to SW. Step 1942 calls the sub-processCalculate Predicate for Children at Step 1834 in FIG. 18.

Step 1944 inspects PiS to determine if it is true. If no, the processproceeds to Step 1950. If yes, SBJ is set to WPRED at Step 1946 andmSubject of mTransient in SM is set to true at Step 1948, and theprocess proceeds to Step 1950. Step 1950 sets RP to WPRED. Step 1952sets WPRED to KWPRED.

Step 1954 resumes the process following Step 1824 in FIG. 18.

FIG. 20 depicts a flow scheme for a Calculate Predicate for SENTENCEStream sub-process in the preferred embodiment of the invention.

The sub-process assumes that SM is set to the TReco which stream needsto be calculated. It also assumes that SM has a mPartOfSPeech value ofSENTENCE. Upon completion, the sub-process will have set ResultPredicate RP to the Predicate holding the conceptual representation ofSM.

In Step 2002, the Calculate Predicate for SENTENCE Stream sub-process iscalled from Step 1820 in FIG. 18. Step 2004 sets HR to true. Step 2006sets Keep Subject KSBJ to SBJ. Step 2008 clears SBJ.

The Find Packet sub-process may have a slightly different behaviordepending if Find Packet Exclusion FPE is true or false. When true, FindPacket shall never set Find Packet Result FPR to the same value asbefore until Find Packet Exclusion List is cleared. If FPE is false,there are no restrictions on what value may be set in FPR.

In Step 2010, FPE is set to true. Step 2012 sets Keep Stream KS toStream. Step 2014 sets DIR to SAMENODE. Step 2016 sets DPT toSAMELEVELORLOWER. Step 2018 sets POSS to “NOUN_PHRASE”. Step 2020 callsthe Find Packet sub-process at Step 2102 in FIG. 21.

In Step 2022, FPR is inspected to determine if it is clear. FPR shouldhave been set by the Find Packet sub-process at Step 2020. If yes, theprocess proceeds to Step 2054. If FPR is not clear at Step 2022, Step2024 sets SM to FPR. Step 2026 sets SM to mParent of mTransient in SM.Step 2028 inspects SM to determine if it is clear. If yes, the processproceeds to Step 2050 where SM is set to KS and the process proceedsthen to Step 2052. If not, Step 2030 inspects mPartOfSpeech in SM todetermine if it is SENTENCE. If not, Step 2026 is reprocessed. If so,Step 2032 sets Subject Search SS to true. Step 2034 sets FPE to false.Step 2036 sets SM to FPR. Step 2038 calls the sub-process CalculatePredicate for Stream at Step 1802 in FIG. 18.

In Step 2040, FPE is set to true. Step 2042 sets SS to false. Step 2044inspects SBJ to determine if it is clear. If no, the process proceeds toStep 2048. If yes, Step 2046 applies the Find Packet Exclusion by addingthe value of FPR to the list of values that FPR may not be set to andthe process proceeds to Step 2048. At Step 2048 SM is set to KS, and theprocess proceeds to Step 2052.

In Step 2052, SBJ is inspected to determine if it is clear. If yes, Step2014 is reprocessed. If not, Step 2054 sets FPE to false. Step 2056clears the Find Packet Exclusion list so that every single value isallowed in FPR.

In Step 2058, SBJ is inspected to determine if it is clear. If yes, SBJis set to Report Subject RS at Step 2060 and the process proceeds toStep 2064. If not, RS is set to SBJ at Step 2062 and the processproceeds to Step 2064.

In Step 2064, STC is set to SM. Step 2066 calls the Calculate Stream forChildren sub-process at Step 1834 in FIG. 18. Step 2068 sets SBJ to KS.Step 2070 sets RP to WPRED. Step 2072 resumes the process following Step1820 in FIG. 18.

FIG. 21 depicts a flow scheme for a Find Packet sub-process in thepreferred embodiment of the invention. The Find Packet sub-process setsFPR with the stream provided Current Packet CP, DIR, DPT and POSS. TRecostructures are related to some others in a syntactic hierarchy, as shownin example in Box 2340 of FIG. 23. In order to go from one TReco toanother in a syntactic hierarchy, the Find Packet sub-process is used.

Possible values for DIR are the followings: BACKWARDSAMENODE,PREVIOUSSAMENODE, NEXTSAMENODE, FORWARDSAMENODE, BACKWARDOUTOFNODE,PREVIOUSOUTOFNODE, NEXTOUTOFNODE, FORWARDOUTOFNODE, UPONLY or SAMENODE.

Possible values for DPT are the followings: SAMENODELEVEL,SAMENODELEVELORLOWER, LOWERNODELEVEL, SAMENODELEVELORUPPER,UPPERNODELEVEL, NOLEVELCONSTRAINT or CHILDNODE.

POSS contains a string value that represents the stream criteria to meetin order to be set in FPR prior to completion of the sub-process.Possible criteria are parts of speech and/or spellings and follow thesame syntactic rules as a transform script line between Streamdelimiters.

In Step 2102, the Find Packet sub-process is called from Step 2158 orStep 2164 in FIG. 21, Step 2020 in FIG. 20 or Step 1910, 1916 or 1922 inFIG. 19. Step 2103 clears LI. In Step 2104, POSS is inspected todetermine if it is clear. If yes, the process proceeds to Step 2111. Ifnot, Step 2106 sets CDN to POSS. Step 2108 sets LI to a new TScptLine.Step 2110 calls the sub-process Get Condition Entry at Step 1610 in FIG.16, and the process proceeds to Step 2111.

In Step 2111, Use Packet UP is set to CP and Step 2112 clears FPR.

In Step 2114, DIR is inspected to determine if it is equal to UPONLY andDPT is inspected to determine if it is equal to UPPERNODELEVEL orSAMENODELEVELORUPPER. If no, the process proceeds to Step 2122. If yes,in Step 2116, DPT is inspected to determine if it is equal toUPPERNODELEVEL and UP is inspected to determine if it is not clear. Ifnot, in Step 2117, FPR is inspected to determine if it is not clear andUP is also inspected to determine if it is not clear and the processproceeds to Step 2198. If so, Step 2118 calls the sub-process EvaluatePacket at Step 2202 of FIG. 22. Step 2119 sets FPR to Evaluate PacketResult EPR. Step 2120 sets UP to mParent of mTransient in UP. Steps 2117to 2120 are repeated until FPR is not clear or the highest level in thesyntactic hierarchy has been reached, and the process proceeds to Step2198.

If no at Step 2144, in Step 2122, DIR is inspected to determine if it isequal to PREVIOUSOUTOFNODE or NEXTOUTOFNODE or FORWARDOUTOFNODE and DPTis inspected to determine if it is equal to SAMENODELEVEL. If no, theprocess proceeds to Step 2144. If yes, in Step 2123, mParent ofmTransient in UP is inspected to determine if it is clear. If yes, theprocess proceeds to Step 2198. If no, at Step 2124, Stop Child Index SCIis cleared. Step 2126 clears Child Index CHI.

In Step 2128, DIR is inspected to determine if it is equal toPREVIOUSOUTOFNODE. If yes, Step 2130 sets CHI to mIndexInParent ofmTransient in UP minus one. Step 2131 sets SCI to CHI and the processproceeds to Step 2136.

If DIR is not equal to PREVIOUSOUTOFNODE in Step 2128, Step 2132inspects DIR to determine if it is equal to NEXTOUTOFNODE. If yes, Step2133 sets CHI to mIndexInParent of mTransient in UP plus one. Step 2134sets SCI to CHI and the process proceeds to Step 2136.

If DIR is not equal to NEXTOUTOFNODE at Step 2132, Step 2135 sets CHI tomIndexInParent of mTransient in UP plus one and the process proceeds toStep 2136.

Step 2136 sets Parent PRT to mParent of mTransient in UP.

In Step 2138, SCI is inspected to determine if it is clear or not clearand greater than CHI, and CHI is inspected to determine if it is smallerthan number of mChildren in PRT and FPR is inspected to determine if itis clear. If no, the process proceeds to Step 2198. If yes, Step 2139sets UP to element CHI of mChildren in PRT. Step 2140 calls thesub-process Evaluate Packet at Step 2202 of FIG. 22. Step 2141 sets FPRto EPR and Step 2142 increases the value of CHI by one. Steps 2138 to2142 are repeated until the condition at Step 2138 fails, at which pointthe process proceeds to Step 2198.

At Step 2198, the process resumes at Step 2158 or Step 2164 in FIG. 21,Step 2020 of FIG. 20 or Step 1910, 1916 or 1922 in FIG. 19 depending onwhich step called the sub-process.

If no at Step 2122, in Step 2144, DIR is inspected to determine if it isequal to SAMENODE and DPT is inspected to determine if it is equal toSAMENODELEVELORLOWER or LOWERNODELEVEL or CHILDNODE. If no, the processproceeds to Step 2168. If yes, Step 2145 sets Use Packet Index UPI to 0.

In Step 2146, UPI is inspected to determine if it is smaller than thenumber of mChildren in UP and FPR is inspected to determine if it isclear. If no, the process proceeds to Step 2196. If yes, Step 2148 setsKeep Use Packet KUP to UP. Step 2149 sets UP to the element UPI ofmChildren in UP. Step 2150 calls the Evaluate Packet sub-process at Step2202 of FIG. 22. Step 2152 sets FPR to EPR.

In Step 2153, FPR is inspected to determine if it is clear. If so, Step2154, DPT is inspected to determine if it is CHILDNODE. If not, DIR isset to BACKWARDSAMENODE at Step 2156. Step 2158 calls the sub-processFind Packet at Step 2102 in FIG. 21. Step 2159 inspects FPR to determineif it is clear. If no, the process proceeds to Step 2165. If yes, UP isset to element UPI of mChildren in UP at Step 2160. Step 2162 sets DIRto FORWARDSAMENODE. Step 2164 calls the sub-process Find Packet at Step2102 in FIG. 21. The sub-process then goes to Step 2165.

In Step 2165, UPI is increased by one. Step 2166 sets UP to KUP. Steps2146 to 2166 are repeated until the condition at Step 2146 fails, atwhich point the process proceeds to Step 2196.

If no at Step 2144, in Step 2168, DIR is inspected to determine if it isequal to BACKWARDSAMENODE or FORWARDSAMENODE and DPT is inspected todetermine if it is equal to SAMENODELEVELORLOWER or LOWERNODELEVEL. Ifno, the process proceeds to Step 2196. If yes, Break Level BL is clearedat Step 2169.

In Step 2170, DPT is inspected to determine if it isSAMENODELEVELORLOWER. If yes, BL is set to mLevel of mTransient in UP atStep 2171. If no, Step 2193 inspects DPT to determine if it isLOWERNODELEVEL. If no, the process proceeds to Step 2172. If yes, Step2194 sets BL to rnLevel of mTransient in UP plus one and then invokesStep 2172.

If yes at Step 2170, BL is set to mLevel of mTransient in UP at Step2171 and the process proceeds to Step 2172. In Step 2172, DIR isinspected to determine if it is FORWARDSAMENODE. If not, Step 2174 setsUP to mParent of mTransient in UP. Step 2175 sets Start Index SI tomIndexInParent of mTransient in UP minus one. Step 2176 sets DrillForward DF to false. Step 2178 calls the sub-process Drill for Packet atStep 2222 of FIG. 22. Step 2179 sets FPR to Drill Packet Result DPR andthe process then proceeds to Step 2196.

If DIR is equal to FORWARDSAMENODE at Step 2172, Step 2180 sets KUP toUP. Step 2181 sets UP to mParent of mTransient in UP. Step 2182 sets SIto mIndexInParent of mTransient in UP plus one. Step 2183 sets DF totrue. Step 2184 calls the sub-process Drill for Packet at Step 2222 ofFIG. 22. Step 2185 sets FPR to DPR.

In Step 2186, FPR is inspected to determine if it is clear. If no, theprocess proceeds to Step 2196. If yes, Step 2188 sets UP to KUP. Step2189 sets SI to 0. Step 2190 calls the sub-process Drill for Packet atStep 2222 of FIG. 22. Step 2192 sets FPR to DPR and the process proceedsto Step 2196.

If at Step 2170 DPT is not SAMENODELEVELORLOWER, Step 2193 inspects DPTto determine if it is LOWERNODELEVEL. If so, Step 2194 sets BL to mLevelof mTransient in UP plus one and then invokes Step 2172.

At Step 2196, the process resumes at Step 2158 or Step 2164 in FIG. 21,Step 2020 of FIG. 20 or Step 1910, 1916 or 1922 in FIG. 19, depending onwhich Step called the sub-process.

FIG. 22 depicts a flow scheme for an Evaluate Packet sub-process and aDrill for Packet sub-process in the preferred embodiment of theinvention.

The Evaluate Packet sub-process is used from the Find Packet sub-processto evaluate a stream in regards to the optional condition that waspassed in CDN to Find Packet which generated LI in Steps 2106 to 2110 inFIG. 21. The Evaluate Packet also considers the exclusion list whilebeing interpreted. As a general rule to use the Evaluate Packetsub-process, the exclusion list contains a list of values that EPR maynot be set to.

In Step 2202, the Evaluate Packet sub-process is called from Step 2240in FIG. 20 or Step 2118, 2140 or 2150 in FIG. 21. The Evaluate Packetsub-process assumes that UP was set to the TReco to evaluate and the LIis clear or contains the condition to evaluate.

In Step 2204, EPR is cleared. In Step 2206, LI is inspected to determineif it is clear. If not, Step 2208 Word WRDD is set to UP. Step 2210 thencalls the Test Stream sub-process at Step 1202 in FIG. 12. Step 2212inspects FS to determine if it is true. If yes, Step 2214 is invoked.

If Step 2206 determined that LI is clear, the process proceeds to Step2214. At Step 2214, the exclusion list is inspected to determine if UPis part of it. If no, Step 2216 sets EPR to UP and the process proceedsto Step 2218. If yes at Step 2214, the process proceeds to Step 2218.

Step 2218 resumes the process following Step 2240 in FIG. 22 or Step2118, 2140 or 2150 in FIG. 21, depending on which step called thesub-process.

The Drill for Packet sub-process is also used from the Find Packetsub-process to find a packet in any children, referred to in mChildrenof a TReco structure, or any of its children if not found.

In Step 2222, the Drill for Packet sub-process is called from Step 2250in FIG. 22 or Step 2178, 2184 or 2190 in FIG. 21. The Drill for Packetsub-process assumes that UP was set by the caller to the TReco to startdrilling from, SI is set to the starting index of the mChildren of UP tostart drilling, DF is set to true if the sub-process needs to incrementSI or false if the sub-process needs to decrement SI, BL is set to thebreak level and it is also assumed that LI is cleared or contains thecondition to meet for a stream to be successfully detected. Thesub-process will set Drill for Packet Result DPR to the stream that metconditions provided UP, SI, DF and LI.

In Step 2224, Packet Index PI is set to SI. Step 2228 sets Drill KeepUse Packet DKUP to UP. Step 2230 clears DPR.

In Step 2232, PI is inspected to determine if it is greater or equal to0 and smaller than the number of elements in mChildren of UP and DPR isalso inspected to determine if it is clear. If no, UP is set to DKUP atStep 2260 and the process proceeds to Step 2262. If yes, Step 2234 setsUP to DKUP. Step 2236 sets UP to the element PI of mChildren in UP.

In Step 2238, mLevel of mTransient in UP is inspected to determine if itis greater or equal to BL. If no, the process proceeds to Step 2246. Ifyes, Step 2240 calls the Evaluate Packet sub-process at Step 2202 ofFIG. 22. Step 2242 sets DPR to EPR and the process proceeds to Step2244.

In Step 2244, DPR is inspected to determine if it is clear. If no, theprocess proceeds to Step 2254. If yes, the process proceeds to Step2246.

Step 2246 sets Keep Starting Index KSI to SI. Step 2248 sets SI to 0.Step 2250 calls the Drill for Packet sub-process at Step 2222 in FIG.22. Step 2252 sets SI to KSI and the process proceeds to Step 2254.

If DPR was determined not to be clear at Step 2244 or following Step2252, Step 2254 inspects DF to determine if it is true. If yes, Step2256 increments PI by one and the process proceeds to Step 2232. If no,Step 2258 decreases PI by one and the process proceeds to Step 2232.Step 2232 is then re-invoked until it fails to verify its condition. Atwhich point, Step 2260 sets UP to DKUP. Step 2262 resumes the processfollowing Step 2250 in FIG. 22 or Step 2178, 2184 or 2190 in FIG. 21,depending on which step called the sub-process.

FIG. 23 depicts a flow scheme for the Set Transient Informationsub-processes in the preferred embodiment of the invention. The SetTransient Information sub-process sets all values in mTransient in aTReco so that hierarchical order is made out of a TReco and itsdependants, i.e. the TReco that were used in order to build it inmChildren in TReco. The result is that a hierarchy like the one shown inBox 2340 in FIG. 23 is produced. The programming engineer may then gofrom one stream to another within the hierarchy through the sub-processFind Packet explained in FIG. 21.

In Box 2302, a predetermined and programmed into the system TTransientstructure is defined as a mParent TReco, a mUpMostParent TReco, amIndexInParent number, a mlevel number and a logical value mSubject. TheTTransient structure is used in a mTransient of a TReco. The SetTransient Information sub-process will set the transient information ofSM and its dependants in mChildren in SM.

In Step 2304, the Set Transient Information sub-process is called fromStep 2332 in FIG. 23 or Step 1714 in FIG. 17. Step 2306 sets mSubject ofSM to false.

In Step 2308, mParent of mTransient in SM is inspected to determine ifit is clear. If yes, mUpMostParent of mTransient in SM is set to SM atStep 2310. Step 2312 sets mIndexInParent of mTransient in SM to −1. Step2314 sets mLevel of mTransient in SM to 0 and the process proceeds toStep 2320.

If mParent in mTransient in SM is not clear at Step 2308, Step 2316 setsmUpMostParent of mTransient in SM to mUpMostParent of mParent ofmTransient in SM. Step 2318 sets mLevel of mTransient in SM to mLevel ofmParent of mTransient in SM plus one, and the process proceeds to Step2320.

Step 2320 sets Child Index CHI to 0. Step 2322 inspects mChildren in SMto determine if it contains more than CHI elements. If no, the processproceeds to Step 2338. If yes, Step 2324 sets KS to SM. Step 2326 setsSM to the element CHI in mChildren in SM. Step 2328 sets mParent in SMto KS. Step 2330 sets mIndexInStream of mTransient in SM to CHI. Step2332 calls the sub-process Set Transient Information at Step 2304 ofFIG. 23. Step 2334 sets SM to KS. Step 2336 increments the value of CHIby one. Step 2322 is then re-invoked until CHI becomes equal to thenumber of mChildren in SM. Once CHI is greater or equal to the number ofelements in mChildren in SM, the process proceeds to Step 2338.

In Box 2340, an example of a syntactic hierarchy produced by the SetTransient Information sub-process is shown.

Optimization

Although the detailed description details a fully functional expressionof this invention, the operation of this embodiment may be improved bythe following applications.

-   1. Steps 1036 to Step 1050 in FIG. 10 do not need to be repeated for    each utterance. Instead, those steps could be executed for the first    utterance, and then a logical flag could be set to true to identify    that transform scripts were loaded for future utterances.-   2. By sorting all phonemes in each time-slice from the most probable    ones (highest probability) to the least probable ones (lowest    probability), the words list will consequently be sorted from the    highest score to the lowest score since search paths would have had    process most probable phonemes prior to least probable phonemes. By    having the words list sorted out from the highest scored stream to    the lowest scored stream for streams that start at the same starting    phoneme index, the Syntactic Analysis process will consequently    generate words sequences that are also sorted from the highest    scored to the lowest scored. This is beneficial since no extra    processing is required than sorting phonemes in a single time-slice    of the phoneme stream in order to get syntactic hierarchy that are    also sorted. Each SENTENCE part of speech produced by the Syntactic    Analysis process would then sequentially have been produced in    order, from the most probable based on the Phoneme Recognition    process, to the least probable. Processing SENTENCE parts of speech    in such order is way better than processing them in a trivial order,    since, as described in this invention, conceptual analysis    terminates once it detects its first successful response Predicate.-   3. A Predicate structure could be expressed as a real structure    instead of a string. That structure would hold a mPrimitive string    (containing the primitive) and a one-dimensional array of RoleFiller    structures. Each RoleFiller structure would hold a mRoleName string    (containing the role name) and a mFiller that is either a) a    Predicate structure, b) a string holding a variable name, or c) a    string holding any value.-   4. The Predicate structure described in (3) of this optimization    section would reside in an address in memory. Once a Predicate    Builder script generates a Predicate structure, it would then build    the Predicate structure in memory and add a predefined prefix that    would state the address of where the Predicate structure resides in    memory. That way, in future manipulations of the Predicate    structure, once the predefined prefix containing the address is    detected, instead of rebuilding the Predicate structure, a simple    reference to the existing Predicate structure residing at the    specified address would be requested—consequently saving significant    processing time.-   5. The Phoneme Recognition and Phoneme Stream Analysis processes    could be united into one unique process in such a way that a phoneme    stream would not need to be encoded in the Phoneme Recognition    process, only to be decoded in the Phoneme Stream Analysis process.    Such encoding, as the one shown in the preferred embodiment of the    invention, is useful in order to trace potential weak links related    to Phoneme Recognition, but requires significant processing to    decode when performing Phoneme Stream Analysis. Instead, search    paths management could be processed immediately during Phoneme    Recognition, potentially saving precious time.-   6. Caching of conceptual representations already calculated for    streams would significantly improve performance. For each stream in    a syntactic hierarchy, a caching mechanism could be implemented so    that it would be clear at the start of calculating a Predicate    structure for a given syntactic hierarchy. Once a Predicate    structure was calculated for a stream in the syntactic hierarchy,    the Predicate structure would be stored as a reference from the    stream. That way, if future Predicate Builder script operations    require the same stream to be calculated again, the cached value    would be used instead of the entire process to recalculate and get    to the same Predicate structure as a result.-   7. The Predicate Builder scripting language is an interpreted    language. In order to get better performance from Conceptual    Analysis, a compiler could be written for the Predicate Builder    scripting language. The process of writing compilers is well know to    those skilled in the art and further explanation is not required    since there is nothing processed specially in the Predicate Builder    scripting language described in the invention.-   8. In order to minimize how many sequences of words are successfully    generated during the Syntactic Analysis process, adding the    constraint where only words formed from a unique cluster could be    sequenced would help significantly. That could be specified as a    configuration parameter to the system for select cases. By way of    example and not intending to limit the invention in any manner, a    flight response system could implement that added constraint. Since    only one speaker is expected to utter a command, it is realistic to    expect that speaker to have produced phonemes from a single cluster.-   9. Should a speaker-independent approach using clusters be out of    reach for any given reason to someone using this invention in a    telephony context, enrollment could indeed be allowed. Then, a    technology similar to caller-ID in a telephony environment could    identify the caller prior to speech processing. By assuming that a    specific caller will always initiate the call from a unique phone    number—or at least that caller would have identified which phone    numbers he is potentially going to use—an association to the    speaker's voice model would then be made prior to speech processing    which would not require clusters anymore.-   10. Should the use of phonemes not produce satisfactory results    during the Phoneme Recognition process, triphones that maps to    phonemes could be used instead. Triphones are defined and discussed    in Jurafsky, Daniel and Martin, James H., Speech and language    processing, Prentice Hall, New Jersey, 2000, pages 250-251 and    273-274, the disclosure of which are herein incorporated by    reference in a manner consistent with this disclosure. Those skilled    in the art are well aware of pronunciation differences of phonemes    provided their proximity to other phonemes. Each variation in    phoneme pronunciation is called a triphone. Instead of having a    cluster voice model holding a unique set of value for each phoneme,    it could hold different set of values (one for each triphone of each    phoneme) that are referred by the pattern recognition algorithm. The    Phoneme Recognition process would then proceed at comparing each    triphones to the audio data in the current time-slice. Once one of    them succeed, the phoneme is added to the phoneme stream and other    triphones for the detected phoneme are not processed for that    time-slice since there is no added value at detecting two identical    phonemes for the same time-slice.-   11. During the Phoneme Recognition process, should phoneme detection    not be accurate enough to always recognize a word because some    phonemes are not well detected or some time-slices do not detect a    phoneme when they should, an error tolerant algorithm could be used    to correct such behavior. The error tolerant algorithm could be    implemented in such a way that, as an example, a search path would    not be dropped immediately if it can't forward in the index tree,    instead, it would be dropped only if two consecutive time-slices    can't forward in the index tree. As an example, if a speaker utters    “I'm comin(g) home” without pronouncing the ‘g’ phoneme at the end    of “coming”, an error tolerant algorithm could well have detected    the word “coming” from that utterance even though a phoneme is    missing. So, an error tolerant Phoneme Stream Analysis process would    have dual purposes. First, it would cover many cases where people do    not fully pronounce each word in their utterance, even covering for    many slang cases. Second, it would make the Phoneme Stream Analysis    process more defensive since some phonemes in utterances may be so    imperceptible that a Phoneme Stream Analysis process that is not    error tolerant may have some difficulties processing the speech    input successfully.-   12. Error tolerance during the Phoneme Stream Analysis process may    not be limited to dropped phonemes as stated in the previous point.    It may also be used for a) wrong phonemes, and/or b) dropped    phonemes. Performing such error tolerance would obviously increase    significantly the size of the candidate words list and a revised    scoring mechanism that accounts for candidate words that were    produced as a consequence of the error tolerance would be    beneficial. This extended error tolerance approach would also only    allow one consecutive misrecognized or dropped phoneme before    dropping the search path. Two consecutive errors of different    natures (e.g. one dropped phoneme followed by one extra phoneme)    would also signal the drop of a search path. In order to handle the    wrong phoneme error tolerant scenario, once recognized phonemes were    processed for all search paths in a time-slice, all un-recognized    phonemes would need to be called within that same time-slice so that    search paths go forward when allowed by the dictionary. In order to    handle the extra phoneme error tolerant scenario, once processing of    recognized phonemes is done for a time-slice, promotion of all    search paths that did not contain any prior error tolerance related    error needs to occur.-   13. The Phoneme Recognition process may have some difficulties    detecting some triphones—i.e. some phonemes when they are in    proximity to other phonemes. In which case, the invention could be    adapted for the phoneme to be ignored for targeted words that are    difficult to recognize (create two pronunciations for the same word,    one that has the phoneme and the other that does not), or even to    remove entirely from the invention the triphone or even the phoneme    itself. As an example and not intending to limit the invention in    any manner, if an implementation of the invention has some serious    problem detecting the ‘t’ sound during the Phoneme Recognition    process, the ‘t’ sound could, as an extreme counter measure to that,    be completely ignored. Then all words in the dictionary would need    to have their ‘t’ phoneme removed from their pronunciation, and the    invention would still be successful at identifying each spoken word    although there would be a higher ratio of mismatches for each    positive match.-   14. Because of the way humans form utterances, often hesitating or    even mumbling within an utterance although they are indeed forming a    syntactic organization that can produce a successful conceptual    representation, the dictionary could hold pronunciation for mumbling    words (like ‘eh’ in “I'd like to ‘eh’ know when ‘eh’ flight 600 will    arrive”). This ‘eh’ pronunciation could refer to a word that would    have the spelling “<Eh-INTERJECTION>”, a Predicate Builder script    that is NULL (not holding any meaning related to the word part of    speech pair) and the part of speech INTERJECTION. An INTERJECTION    part of speech would be specially handled during the Syntactic    Analysis process in the sense that a mismatch on an INTERJECTION    part of speech could not make a sequence of words fail for any    transform script line that is being validated. So, the ‘eh’ sound    could be found anywhere in the utterance without risking to    invalidate any syntactic sequence of words. The same approach could    be used more generically for other INTEJECTION words like “please”    as an example. The sentence “I'd like to ‘please’ know when flight    600 will arrive” is valid, as well as the sentence “I'd like to know    when flight 600 will arrive ‘please’”. That is a demonstration of    the fact that ‘please’ is an INTERJECTION part of speech and that it    is desired that the Syntactic Analysis process not to fail sequences    of words because of the presence of an INTERJECTION part of speech    in it.-   15. A top-down parsing algorithm in the Syntactic Analysis process    would significantly improve performance for cases where large    quantity of words needs to be analyzed for syntactically valid    sequences to be formed. Since SENTENCE parts of speech are the only    ones that are truly important to the preferred embodiment of this    invention (so that they can then be analyzed conceptually), a    top-down parsing algorithm would mean that SENTENCE parts of speech    could be formed first (without having to go through previous    sequence validation like NOUN-PHRASE as in the bottom-up parsing    algorithm described in this application). Any top-down parsing    algorithm that is implemented should be flexible enough to enclose    all permutation rules for this invention to enable dictation content    to be processed conceptually. The top-down parsing algorithm would    most probably take the form of a significantly large index structure    that would hold all parts of speech sequences that may generate a    SENTENCE part of speech which would have been produced by analyzing    all transform scripts that could be built following the same rules    as the ones described in the preferred embodiment of this invention.    The top-down algorithm could then refer to that specially built    index structure in order to validate sequences of words so that    SENTENCE parts of speech are built immediately—without preliminary    steps like creating NOUN-PHRASE parts of speech as required in a    bottom-up parsing. Once a SENTENCE part of speech was built    successfully, the Syntactic Analysis process could then apply a    bottom-up parsing so that enclosed parts of speech are also    generated and that conceptual analysis could process equally as if    only a bottom-up parsing algorithm was involved.-   16. For the invention to be used to dictate freely content in a word    processor, any Hidden-Markov-Model implementation—where the N-best    words are used as input for Syntactic Analysis, and N-best words of    sequences of words returned by HMM algorithm are also taken into    consideration—or the Phoneme Recognition process described in this    application could be used to generate the words list while keeping    trace of each word starting phoneme index in the phoneme stream. The    Syntactic Analysis process would then validate words sequences as    described in this invention. Once SENTENCE parts of speech are    identified, there are two major improvements over state of the art    dictation speech recognition technology that do not use conceptual    analysis: a) accuracy would improve since syntactic and conceptual    aspects of the speech would be taken into consideration during    speech processing, and b) while getting a valid concept, as a    residual is the syntactic organization that was used to produce such    valid concept; the programming engineer could then use that    syntactic organization in order to infer punctuation requirements    needed as part of dictated content—consequently generating    punctuation in the dictated content without having the speaker    explicitly dictating punctuation.-   17. Bridging, as explained in FIG. 4 to FIG. 6, may also be used for    phonemes that have close pronunciations. Step 564, 634 and 1108    would need to be modified in such a way that some predefined    phonemes could be identified as a potential bridging that needs to    happen if followed by some other predefined phonemes. As an example    and not intending to limit the invention in any manner, if the ‘s’    sound of ‘this’ is found to be close enough from the ‘z’ sound of    ‘zoo’, in Step 634, while detecting that the last phoneme of a    candidate word is ‘s’, it could set the ‘s’ entry in BL to true as    well as the ‘z’ entry for the ending phoneme index in BL. Step 1108    would also need to implement a simple mechanism (probably a static    mapping table of bridging phoneme sets) where the two phonemes are    identified as ones that may have generated a bridge. That way, a    sequence like “this zoo is near” would be successfully recognized    for speakers that tend to perform more bridging than others.

EXAMPLES

The following examples are intended to further illustrate theapplication of the invention in a limited context, an airline responsesystem, and is not intended to limit the invention in any manner.

Numerous inquiries were input into a sample airline response inquirysystem according to the invention. For purposes of illustration andtesting of the building of conceptually adequate responses to thoseinquiries, the following database was created to provide typicalresponsive reference data that may be found in such an application.

-   Flight Number: 122-   Company: US-   Origin Airport: DEN-   Destination Airport: DFW-   Status: INFLIGHT-   Initial Departure Time: 13:15-   Revised Departure Time: 13:23-   Departure Gate: 15-   Initial Arrival Time: 15:13-   Revised Arrival Time: 15:19-   Arrival Gate: B 6-   Flight Number: 320-   Company: AA-   Origin Airport: LAS-   Destination Airport: DFW-   Status: INFLIGHT-   Initial Departure Time: 13:20-   Revised Departure Time: 14:35-   Departure Gate: E 42-   Initial Arrival Time: 16:20-   Revised Arrival Time: 16:15-   Arrival Gate: B 2-   Flight Number: 1547-   Company: DL-   Origin Airport: LAX-   Destination Airport: DFW-   Status: ARRIVED-   Initial Departure Time: 10:22-   Revised Departure Time: 10:43-   Departure Gate: 7-   Initial Arrival Time: 13:30-   Revised Arrival Time: 14:31-   Arrival Gate: A 10-   Flight Number: 1271-   Company: UA-   Origin Airport: BOS-   Destination Airport: DFW-   Status: ARRIVED-   Initial Departure Time: 9:10-   Revised Departure Time: 9:25-   Departure Gate: C 76-   Initial Arrival Time: 14:10-   Revised Arrival Time: 14:25-   Arrival Gate: C 4-   Flight Number: 600-   Company: UA-   Origin Airport: JFK-   Destination Airport: DFW-   Status: ARRIVED-   Initial Departure Time: 8:52-   Revised Departure Time: 8:59-   Departure Gate: B 21-   Initial Arrival Time: 14:20-   Revised Arrival Time: 14:32-   Arrival Gate: B 2

Example 1

The following inquiry was input into an embodiment of the system andmethod of the invention, with the corresponding response based on thereference data contained from the flight database. The data wasprocessed using a 2.4 GHz Pentium 4 computer that has 1 GB of RAM.

-   Q: Is flight six hundred delayed?-   A: United Airline flight 600 arrived at 2 32 PM and was late by 12    minutes.    Syntactic Organization:

Spelling: is flight 600 delayed is flight 600 delayed <- [SENTENCE,SENTENCE CONSTRUCTION 1, level 0, index −1] is flight 600 delayed <-[VERB_PHRASE, VERB PHRASE CONSTRUCTION 9, level 1, index 0] is flight600 <- [VERB_PHRASE, VERB PHRASE CONSTRUCTION 1, level 2, index 0] is <-[VERB, WORD, level 3, index 0] flight 600 <- [NOUN_PHRASE, PLAIN NOUNPHRASE CONSTRUCTION, level 3, index 1] flight 600 <- [NOUN, FLIGHTINTEGRATION, level 4, index 0] flight 600 <- [FLIGHT, FLIGHTIDENTIFICATION CONSTRUCTION 2, level 5, index 0] flight <- [NOUN, WORD,level 6, index 0] 600 <- [CARDINAL_NUMBER, WORD, level 6, index 1] 6 <-[CARDINAL_NUMBER, WORD, level 7, index 0] 100 <- [CARDINAL_NUMBER, WORD,level 7, index 1] delayed <- [ADJECTIVE_PHRASE, ADJECTIVE PHRASECONSTRUCTION 1, level 2, index 1] delayed <- [ADJECTIVE, WORD, level 3,index 0]Conceptual Analysis Result:

[MOOD (CLASS:INTEROGATIVE) (OBJECT: [PP (CLASS:VEHICLE) (TYPE:AIRPLANE)(COMPANY:UA) (NUMBER:600) (ORIGIN:JFK) (DESTINATION:DFW)(STATUS:ARRIVED) (DEPARTURETIME:8:59) (ARRIVALTIME:14:32)(INITIALDEPARTURETIME:8:52) (INITIALARRIVALTIME:14:20) (DEPARTUREGATE:B21) (ARRIVALGATE:B 2) (SPOKENCOMPANY:NONE)]) (QUERY:[AIRLINEPOSTANALYSIS (OPERATION: [REPORT (VALUE:DELTASTATUS) (OBJECT:[PP (CLASS:VEHICLE) (TYPE:AIRPLANE) (COMPANY:UA) (NUMBER:600)(ORIGIN:JFK) (DESTINATION:DFW) (STATUS:ARRIVED) (DEPARTURETIME:8:59)(ARRIVALTIME:14:32) (INITIALDEPARTURETIME:8:52)(INITIALARRIVALTIME:14:20) (DEPARTUREGATE:B 21) (ARRIVALGATE:B 2)(SPOKENCOMPANY:NONE)])])]) (TIME_OF_ANALYSIS:Thu Jun 19 20:20:38 2003)]Time Spent:

streaming and stream analysis 15 ms syntactic analysis  0 ms conceptualanalysis 16 ms

The system arrived at the indicated response after approximately 31 msfrom the time of the inquiry input.

Example 2

Similar to Example 1, the following inquiry was input into the samesystem, with the indicated response.

-   Q: When did flight one twenty two leave?-   A: US airways flight 122 left at 1 23 PM.    Syntactic Organization:

Spelling: when did flight 122 leave when did flight 122 leave <-[SENTENCE, SENTENCE CONSTRUCTION 1, level 0, index −1] when did flight122 leave <- [VERB_PHRASE, VERB PHRASE CONSTRUCTION 10, level 1, index0] when <- [WH_PRONOUN, WORD, level 2, index 0] did flight 122 leave <-[VERB_PHRASE, VERB PHRASE CONSTRUCTION 7, level 2, index 1] did <-[VERB, WORD, level 3, index 0] flight 122 <- [NOUN_PHRASE, PLAIN NOUNPHRASE CONSTRUCTION, level 3, index 1] flight 122 <- [NOUN, FLIGHTINTEGRATION, level 4, index 0] flight 122 <- [FLIGHT, FLIGHTIDENTIFICATION CONSTRUCTION 2, level 5, index 0] flight <- [NOUN, WORD,level 6, index 0] 122 <- [CARDINAL_NUMBER, WORD, level 6, index 1] 1 <-[CARDINAL_NUMBER, WORD, level 7, index 0] 22 <- [CARDINAL_NUMBER, WORD,level 7, index 1] 20 <- [CARDINAL_NUMBER, WORD, level 8, index 0] 2 <-[CARDINAL_NUMBER, WORD, level 8, index 1] leave <- [VERB, WORD, level 3,index 2]Conceptual Analysis Result:

[MOOD (CLASS:INTEROGATIVE) (QUERY: [AIRLINEPOSTANALYSIS (OPERATION:[REPORT (VALUE:DEPARTURETIME) (OBJECT: [PP (CLASS:VEHICLE)(TYPE:AIRPLANE) (COMPANY:US) (NUMBER:122) (ORIGIN:DEN) (DESTINATION:DFW)(STATUS:INFLIGHT) (DEPARTURETIME:13:23) (ARRIVALTIME:15:19)(INITIALDEPARTURETIME:13:15) (INITIALARRIVALTIME:15:13)(DEPARTUREGATE:15) (ARRIVALGATE:B 6) (SPOKENCOMPANY:NONE)])])]) (OBJECT:[PP (CLASS:VEHICLE) (TYPE:AIRPLANE) (COMPANY:US) (NUMBER:122)(ORIGIN:DEN) (DESTINATION:DFW) (STATUS:INFLIGHT) (DEPARTURETIME:13:23)(ARRIVALTIME:15:19) (INITIALDEPARTURETIME:13:15)(INITIALARRIVALTIME:15:13) (DEPARTUREGATE:15) (ARRIVALGATE:B 6)(SPOKENCOMPANY:NONE)]) (ASSUMPTION_ON_TIME_OF_EVENT:< PRESENT TIME)(TIME_OF_ANALYSIS:Thu Jun 19 20:20:40 2003)]Time Spent:

streaming and stream analysis 16 ms syntactic analysis 15 ms conceptualanalysis 32 ms

The total approximate analysis time was 63 ms.

Example 3

The following inquiry was made into the same system as Examples 1 and 2,with the indicated response.

-   Q: Has united airlines flight six hundred arrived yet?-   A: Yes, United Airline flight 600 arrived at 2 32 PM.    Syntactic Organization:

Spelling: has United airlines flights 600 arrived yet has Unitedairlines flights 600 arrived yet <- [SENTENCE, SENTENCE CONSTRUCTION 1,level 0, index −1] has United airlines flights 600 arrived yet <-[VERB_PHRASE, VERB PHRASE CONSTRUCTION 5, level 1, index 0] has <-[VERB, WORD, level 2, index 0] United airlines flights 600 <-[NOUN_PHRASE, PLAIN NOUN PHRASE CONSTRUCTION, level 2, index 1] Unitedairlines flights 600 <- [NOUN, FLIGHT INTEGRATION, level 3, index 0]United airlines flights 600 <- [FLIGHT, FLIGHT IDENTIFICATIONCONSTRUCTION 2, level 4, index 0] United airlines <- [AIRLINE, AIRLINEIDENTIFICATION, level 5, index 0] United <- [AIRLINE, WORD, level 6,index 0] airlines <- [NOUN, WORD, level 6, index 1] flights <- [NOUN,WORD, level 5, index 1] 600 <- [CARDINAL_NUMBER, WORD, level 5, index 2]6 <- [CARDINAL_NUMBER, WORD, level 6, index 0] 100 <- [CARDINAL_NUMBER,WORD, level 6, index 1] arrived yet <- [GERUNDIVE_PHRASE, GERUNDIVEPHRASE CONSTRUCTION, level 2, index 2] arrived <- [GERUNDIVE_VERB,GERUNDIVE ED, level 3, index 0] arrived <- [VERB, WORD, level 4, index0] yet <- [ADVERB, WORD, level 3, index 1]Conceptual Analysis Result:

[MOOD (CLASS:INTEROGATIVE) (OBJECT: [PP (CLASS:VEHICLE) (TYPE:AIRPLANE)(COMPANY:UA) (NUMBER:600) (ORIGIN:JFK) (DESTINATION:DFW)(STATUS:ARRIVED) (DEPARTURETIME:8:59) (ARRIVALTIME:14:32)(INITIALDEPARTURETIME:8:52) (INITIALARRIVALTIME:14:20) (DEPARTUREGATE:B21) (ARRIVALGATE:B 2) (SPOKENCOMPANY:UA)]) (QUERY: [AIRLINEPOSTANALYSIS(OPERATION: [REPORT (VALUE:STATUSARRIVED) (OBJECT: [PP (CLASS:VEHICLE)(TYPE:AIRPLANE) (COMPANY:UA) (NUMBER:600) (ORIGIN:JFK) (DESTINATION:DFW)(STATUS:ARRIVED) (DEPARTURETIME:8:59) (ARRIVALTIME:14:32)(INITIALDEPARTURETIME:8:52) (INITIALARRIVALTIME:14:20) (DEPARTUREGATE:B21) (ARRIVALGATE:B 2) (SPOKENCOMPANY:UA)])])])(ASSUMPTION_ON_TIME_OF_EVENT:< PRESENT_TIME) (TIME_OF_ANALYSIS:Thu Jun19 20:20:41 2003)]Time Spent:

streaming and stream analysis 16 ms syntactic analysis 15 ms conceptualanalysis 16 ms

The result was reached in approximately 47 ms.

Example 4

Using the same system as Examples 1, 2 and 3, the following inquiry wasinput and the indicated response was returned.

-   Q: Will flight 600 arrive before long?-   A: United Airline flight 600 already arrived 20 minutes ago.    Syntactic Organization:

Spelling: will flights 600 arrive before long will flights 600 arrivebefore long <- [SENTENCE, SENTENCE CONSTRUCTION 1, level 0, index −1]will flights 600 arrive before long <- [VERB_PHRASE, VERB PHRASECONSTRUCTION 9, level 1, index 0] will flights 600 arrive <-[VERB_PHRASE, VERB PHRASE CONSTRUCTION 5, level 2, index 0] will <-[VERB, WORD, level 3, index 0] flights 600 <- [NOUN_PHRASE, PLAIN NOUNPHRASE CONSTRUCTION, level 3, index 1] flights 600 <- [NOUN, FLIGHTINTEGRATION, level 4, index 0] flights 600 <- [FLIGHT, FLIGHTIDENTIFICATION CONSTRUCTION 2, level 5, index 0] flights <- [NOUN, WORD,level 6, index 0] 600 <- [CARDINAL_NUMBER, WORD, level 6, index 1] 6 <-[CARDINAL_NUMBER, WORD, level 7, index 0] 100 <- [CARDINAL_NUMBER, WORD,level 7, index 1] arrive <- [GERUNDIVE_PHRASE, GERUNDIVE PHRASECONSTRUCTION, level 3, index 2] arrive <- [GERUNDIVE_VERB, WORD, level4, index 0] before long <- [ADJECTIVE_PHRASE, ADJECTIVE PHRASECONSTRUCTION 1, level 2, index 1] before long <- [ADJECTIVE, WORD, level3, index 0]Conceptual Analysis Result:

[MOOD (CLASS:INTEROGATIVE) (OBJECT: [PP (CLASS:VEHICLE) (TYPE:AIRPLANE)(COMPANY:UA) (NUMBER:600) (ORIGIN:JFK) (DESTINATION:DFW)(STATUS:ARRIVED) (DEPARTURETIME:8:59) (ARRIVALTIME:14:32)(INITIALDEPARTURETIME:8:52) (INITIALARRIVALTIME:14:20) (DEPARTUREGATE:B21) (ARRIVALGATE:B 2) (SPOKENCOMPANY:NONE)]) (QUERY:[AIRLINEPOSTANALYSIS (OPERATION: [REPORT (VALUE:TIMETOARRIVAL) (OBJECT:[PP (CLASS:VEHICLE) (TYPE:AIRPLANE) (COMPANY:UA) (NUMBER:600)(ORIGIN:JFK) (DESTINATION:DFW) (STATUS:ARRIVED) (DEPARTURETIME:8:59)(ARRIVALTIME:14:32) (INITIALDEPARTURETIME:8:52)(INITIALARRIVALTIME:14:20) (DEPARTUREGATE:B 21) (ARRIVALGATE:B 2)(SPOKENCOMPANY:NONE)])])]) (ASSUMPTION_ON_TIME_OF_EVENT:> PRESENT_TIME)(TIME_OF_ANALYSIS:Thu Jun 19 20:20:41 2003)]Time Spent:

streaming and stream analysis 15 ms syntactic analysis 16 ms conceptualanalysis 15 ms

This result was returned in approximately 46 ms.

Example 5

Using the same system, as Examples 1, 2, 3 and 4, the following inquirywas input and the indicated response was returned.

-   Q: From which city and at what time did flight 600 take off?-   A: United Airline flight 600 originated from New York at 8 59 AM.    Syntactic Organization:

Spelling: from which city and at what time did flights 600 take off fromwhich city and at what time did flights 600 take off <- [SENTENCE,SENTENCE CONSTRUCTION 1, level 0, index −1] from which city and at whattime <- [WH_NP, WH_NP CONSTRUCTION 4, level 1, index 0] from which city<- [WH_NP, WH_NP CONSTRUCTION 1, level 2, index 0] from which <-[WH_PRONOUN, WORD, level 3, index 0] city <- [NOUN_PHRASE, PLAIN NOUNPHRASE CONSTRUCTION, level 3, index 1] city <- [NOUN, WORD, level 4,index 0] and <- [CONJUNCTION, WORD, level 2, index 1] at what time <-[WH_NP, WORD, level 2, index 2] did flights 600 take off <-[VERB_PHRASE, VERB PHRASE CONSTRUCTION 7, level 1, index 1] did <-[VERB, WORD, level 2, index 0] flights 600 <- [NOUN_PHRASE, PLAIN NOUNPHRASE CONSTRUCTION, level 2, index 1] flights 600 <- [NOUN, FLIGHTINTEGRATION, level 3, index 0] flights 600 <- [FLIGHT, FLIGHTIDENTIFICATION CONSTRUCTION 2, level 4, index 0] flights <- [NOUN, WORD,level 5, index 0] 600 <- [CARDINAL_NUMBER, WORD, level 5, index 1] 6 <-[CARDINAL_NUMBER, WORD, level 6, index 0] 100 <- [CARDINAL_NUMBER, WORD,level 6, index 1] take off <- [VERB, WORD, level 2, index 2]Conceptual Analysis Result:

[AND (VALUE1: [MOOD (CLASS:INTEROGATIVE) (QUERY: [AIRLINEPOSTANALYSIS(OPERATION: [REPORT (VALUE:DEPARTURECITY) (OBJECT: [PP (CLASS:VEHICLE)(TYPE:AIRPLANE) (COMPANY:UA) (NUMBER:600) (ORIGIN:JFK) (DESTINATION:DFW)(STATUS:ARRIVED) (DEPARTURETIME:8:59) (ARRIVALTIME:14:32)(INITIALDEPARTURETIME:8:52) (INITIALARRIVALTIME:14:20) (DEPARTUREGATE:B21) (ARRIVALGATE:B 2) (SPOKENCOMPANY:NONE)])])]) (OBJECT: [PP(CLASS:VEHICLE) (TYPE:AIRPLANE) (COMPANY:UA) (NUMBER:600) (ORIGIN:JFK)(DESTINATION:DFW) (STATUS:ARRIVED) (DEPARTURETIME:8:59)(ARRIVALTIME:14:32) (INITIALDEPARTURETIME:8:52)(INITIALARRIVALTIME:14:20) (DEPARTUREGATE:B 21) (ARRIVALGATE:B 2)(SPOKENCOMPANY:NONE)])]) (VALUE2: [MOOD (CLASS:INTEROGATIVE) (QUERY:[AIRLINEPOSTANALYSIS (OPERATION: [REPORT (VALUE:DEPARTURETIME) (OBJECT:[PP (CLASS:VEHICLE) (TYPE:AIRPLANE) (COMPANY:UA) (NUMBER:600)(ORIGIN:JFK) (DESTINATION:DFW) (STATUS:ARRIVED) (DEPARTURETIME:8:59)(ARRIVALTIME:14:32) (INITIALDEPARTURETIME:8:52)(INITIALARRIVALTIME:14:20) (DEPARTUREGATE:B 21) (ARRIVALGATE:B 2)(SPOKENCOMPANY:NONE)])])]) (OBJECT: [PP (CLASS:VEHICLE) (TYPE:AIRPLANE)(COMPANY:UA) (NUMBER:600) (ORIGIN:JFK) (DESTINATION:DFW)(STATUS:ARRIVED) (DEPARTURETIME:8:59) (ARRIVALTIME:14:32)(INITIALDEPARTURETIME:8:52) (INITIALARRIVALTIME:14:20) (DEPARTUREGATE:B21) (ARRIVALGATE:B 2) (SPOKENCOMPANY:NONE)])])(ASSUMPTION_ON_TIME_OF_EVENT:< PRESENT_TIME) (TIME_OF_ANALYSIS:Thu Jun19 20:20:41 2003)]Time Spent:

streaming and stream analysis 31 ms syntactic analysis 16 ms conceptualanalysis 15 ms

The result was returned in approximately 62 ms.

Example 6

Using the same system, as Examples 1, 2, 3, 4 and 5, the followinginquiry was input and the indicated response was returned.

-   Q: When and where did flight six hundred arrive from New York and    how late was the departure of flight three twenty?-   A: United Airline flight 600 arrived from New York at 2 32 PM at    gate B 2 of Dallas Fort Worth International Airport in Dallas.    American Airline flight 320 left at 2 35 PM and was late by 1 hour    15 minutes.    Syntactic Organization:

Spelling: when and where did flight 600 arrive from New York and howlate was the departure of flight 320 when and where did flight 600arrive from New York and how late was the departure of flight 320 <-[SENTENCE, SENTENCE CONSTRUCTION 4, level 0, index −1] when and wheredid flight 600 arrive from New York <- [SENTENCE, SENTENCE CONSTRUCTION1, level 1, index 0] when and where did flight 600 arrive from New York<- [VERB_PHRASE, VERB PHRASE CONSTRUCTION 10, level 2, index 0] when andwhere <- [WH_PRONOUN, WH_PRONOUN CONSTRUCTION 1, level 3, index 0] when<- [WH_PRONOUN, WORD, level 4, index 0] and <- [CONJUNCTION, WORD, level4, index 1] where <- [WH_PRONOUN, WORD, level 4, index 2] did flight 600arrive from New York <- [VERB_PHRASE, VERB PHRASE CONSTRUCTION 5, level3, index 1] did <- [VERB, WORD, level 4, index 0] flight 600 <-[NOUN_PHRASE, PLAIN NOUN PHRASE CONSTRUCTION, level 4, index 1] flight600 <- [NOUN, FLIGHT INTEGRATION, level 5, index 0] flight 600 <-[FLIGHT, FLIGHT IDENTIFICATION CONSTRUCTION 2, level 6, index 0] flight<- [NOUN, WORD, level 7, index 0] 600 <- [CARDINAL_NUMBER, WORD, level7, index 1] 6 <- [CARDINAL_NUMBER, WORD, level 8, index 0] 100 <-[CARDINAL_NUMBER, WORD, level 8, index 1] arrive <- [GERUNDIVE_PHRASE,GERUNDIVE PHRASE CONSTRUCTION, level 4, index 2] arrive <-[GERUNDIVE_VERB, WORD, level 5, index 0] from New York <-[PREPOSITION_PHRASE, PREPOSITION PHRASE CONSTRUCTION 1, level 4, index3] from <- [PREPOSITION, WORD, level 5, index 0] New York <-[NOUN_PHRASE, PLAIN NOUN PHRASE CONSTRUCTION, level 5, index 1] New York<- [NOUN, CITY INTEGRATION, level 6, index 0] New York <- [CITY, WORD,level 7, index 0] and <- [CONJUNCTION, WORD, level 1, index 1] how latewas the departure of flight 320 <- [SENTENCE, SENTENCE CONSTRUCTION 1,level 1, index 2] how late <- [WH_NP, WH_NP CONSTRUCTION 2, level 2,index 0] how <- [WH_PRONOUN, WORD, level 3, index 0] late <- [ADJECTIVE,WORD, level 3, index 1] was the departure of flight 320 <- [VERB_PHRASE,VERB PHRASE CONSTRUCTION 1, level 2, index 1] was <- [VERB, WORD, level3, index 0] the departure <- [NOUN_PHRASE, PLAIN NOUN PHRASECONSTRUCTION, level 3, index 1] the <- [DEFINITE_ARTICLE, WORD, level 4,index 0] departure <- [NOUN, WORD, level 4, index 1] of flight 320 <-[PREPOSITION_PHRASE, PREPOSITION PHRASE CONSTRUCTION 1, level 3, index2] of <- [PREPOSITION, WORD, level 4, index 0] flight 320 <-[NOUN_PHRASE, PLAIN NOUN PHRASE CONSTRUCTION, level 4, index 1] flight320 <- [NOUN, FLIGHT INTEGRATION, level 5, index 0] flight 320 <-[FLIGHT, FLIGHT IDENTIFICATION CONSTRUCTION 2, level 6, index 0] flight<- [NOUN, WORD, level 7, index 0] 320 <- [CARDINAL_NUMBER, WORD, level7, index 1] 3 <- [CARDINAL_NUMBER, WORD, level 8, index 0] 20 <-[CARDINAL_NUMBER, WORD, level 8, index 1]Conceptual Analysis Result:

[AND (VALUE1: [AND (VALUE1: [MOOD (CLASS:INTEROGATIVE) (QUERY:[AIRLINEPOSTANALYSIS (OPERATION: [REPORT (VALUE:ARRIVALTIME) (OBJECT:[PP (CLASS:VEHICLE) (TYPE:AIRPLANE) (COMPANY:UA) (NUMBER:600)(ORIGIN:JFK) (DESTINATION:DFW) (STATUS:ARRIVED) (DEPARTURETIME:8:59)(ARRIVALTIME:14:32) (INITIALDEPARTURETIME:8:52)(INITIALARRIVALTIME:14:20) (DEPARTUREGATE:B 21) (ARRIVALGATE:B 2)(SPOKENCOMPANY:NONE)])])]) (OBJECT: [PP (CLASS:VEHICLE) (TYPE:AIRPLANE)(COMPANY:UA) (NUMBER:600) (ORIGIN:JFK) (DESTINATION:DFW)(STATUS:ARRIVED) (DEPARTURETIME:8:59) (ARRIVALTIME:14:32)(INITIALDEPARTURETIME:8:52) (INITIALARRIVALTIME:14:20) (DEPARTUREGATE:B21) (ARRIVALGATE:B 2) (SPOKENCOMPANY:NONE)])]) (VALUE2: [MOOD(CLASS:INTEROGATIVE) (QUERY: [AIRLINEPOSTANALYSIS (OPERATION: [REPORT(VALUE:ARRIVALLOCATION) (OBJECT: [PP (CLASS:VEHICLE) (TYPE:AIRPLANE)(COMPANY:UA) (NUMBER:600) (ORIGIN:JFK) (DESTINATION:DFW)(STATUS:ARRIVED) (DEPARTURETIME:8:59) (ARRIVALTIME:14:32)(INITIALDEPARTURETIME:8:52) (INITIALARRIVALTIME:14:20) (DEPARTUREGATE:B21) (ARRIVALGATE:B 2) (SPOKENCOMPANY:NONE)])])]) (OBJECT: [PP(CLASS:VEHICLE) (TYPE:AIRPLANE) (COMPANY:UA) (NUMBER:600) (ORIGIN:JFK)(DESTINATION:DFW) (STATUS:ARRIVED) (DEPARTURETIME:8:59)(ARRIVALTIME:14:32) (INITIALDEPARTURETIME:8:52)(INITIALARRIVALTIME:14:20) (DEPARTUREGATE:B 21) (ARRIVALGATE:B 2)(SPOKENCOMPANY:NONE)])]) (ASSUMPTION_ON_TIME_OF_EVENT:< PRESENT_TIME)(EXTRA: [AIRLINEPOSTANALYSIS (OPERATION: [VERIFY (ORIGIN: [CITY(CITYCODE:NEWYORK) (VALUE: [AIRPORT (AIRPORTCODE:JFK) (AIRPORTNAME:JohnF Kennedy International Airport)]) (VALUE: [AIRPORT (AIRPORTCODE:NWK)(AIRPORTNAME:Newark International Airport)])]) (OBJECT: [PP(CLASS:VEHICLE) (TYPE:AIRPLANE) (COMPANY:UA) (NUMBER:600) (ORIGIN:JFK)(DESTINATION:DFW) (STATUS:ARRIVED) (DEPARTURETIME:8:59)(ARRIVALTIME:14:32) (INITIALDEPARTURETIME:8:52)(INITIALARRIVALTIME:14:20) (DEPARTUREGATE:B 21) (ARRIVALGATE:B 2)(SPOKENCOMPANY:NONE)])])])]) (VALUE2: [MOOD (CLASS:INTEROGATIVE) (QUERY:[AIRLINEPOSTANALYSIS (OPERATION: [AIRLINEPOSTANALYSIS (OPERATION: [REPORT  (VALUE:DEPARTUREDELTASTATUS) (OBJECT: [PP (CLASS:VEHICLE)(TYPE:AIRPLANE) (COMPANY:AA) (NUMBER:320) (ORIGIN:LAS) (DESTINATION:DFW)(STATUS:INFLIGHT) (DEPARTURETIME:14:35) (ARRIVALTIME:16:15)(INITIALDEPARTURETIME:13:20) (INITIALARRIVALTIME:16:20) (DEPARTUREGATE:E42) (ARRIVALGATE:B 2) (SPOKENCOMPANY:NONE)])])]) (OBJECT: [PP(CLASS:VEHICLE) (TYPE:AIRPLANE) (COMPANY:AA) (NUMBER:320) (ORIGIN:LAS)(DESTINATION:DFW) (STATUS:INFLIGHT) (DEPARTURETIME:14:35)(ARRIVALTIME:16:15) (INITIALDEPARTURETIME:13:20)(INITIALARRIVALTIME:16:20) (DEPARTUREGATE:E 42) (ARRIVALGATE:B 2)(SPOKENCOMPANY:NONE)])])]) (OBJECT: [PP (CLASS:VEHICLE) (TYPE:AIRPLANE)(COMPANY:AA) (NUMBER:320) (ORIGIN:LAS) (DESTINATION:DFW)(STATUS:INFLIGHT) (DEPARTURETIME:14:35) (ARRIVALTIME:16:15)(INITIALDEPARTURETIME:13:20) (INITIALARRIVALTIME:16:20) (DEPARTUREGATE:E42) (ARRIVALGATE:B 2) (SPOKENCOMPANY:NONE)])(ASSUMPTION_ON_TIME_OF_EVENT:< PRESENT_(—) TIME)]) (TIME_OF_ANALYSIS:ThuJun 19 20:20:39 2003)]Time Spent:

streaming and stream analysis 31 ms syntactic analysis 31 ms conceptualanalysis 828 ms 

The result was returned in approximately 890 ms.

The foregoing embodiments have been presented for the purpose ofillustration and description only and are not to be construed aslimiting the scope of the invention in any way. The scope of theinvention is to be determined from the claims appended hereto.

1. A method of processing speech, comprising: generating a list ofcandidate words for at least one set of phonemes, each candidate wordhaving a pronunciation boundary, from a phoneme analysis of a receivedspeech input; permuting at least one member of the list of candidatewords for the at least one set of phonemes to generate a plurality ofpotential syntactic structures which are valid in accordance with a setof syntactic rules, while respecting pronunciation boundaries of thecandidate words; generating a plurality of valid syntactic sequences ofwords from the permuted candidate words and potential syntacticstructures; processing a speech input to identify a plurality ofsyntactic sequences of words, the syntactic sequences of wordscomprising the candidate words, the candidate words and the syntacticsequences of words each having at least one associated part of speech;deriving one or more conceptual representations lion at least one of thesyntactic sequences of words; and formulating one or more responses tothe speech input based on at least one conceptual representation.
 2. Themethod of claim 1, wherein the step of formulating the responsecomprises processing the conceptual representation in relation toreference data.
 3. The method of claim 2, wherein the reference datacomprises a database.
 4. The method of claim 2, wherein the referencedata comprises a physical measurement.
 5. The method of claim 2, furthercomprising executing a command to communicate at least one of theresponses.
 6. The method of claim 5, wherein the step of communicatingthe response comprises at least one of an audio response, a textresponse, a visual response or a mechanical response.
 7. The method ofclaim 6, further comprising identifying one or more inquiry anomalies inthe speech input for at least one of the syntactic sequences of words.8. The method of claim 7, wherein the inquiry anomaly comprises aninconsistency between the conceptual representations and at least someof the reference data.
 9. The method of claim 8, wherein inquiryanomalies are given a scaled designation relating to the magnitude ofthe inquiry anomaly and ranked according to the sealed designation. 10.The method of claim 8, further comprising associating one or moreinquiry anomaly indicators relating to the rank of the inquiry anomalywith the conceptual representations.
 11. The method of claim 10, whereinthe communicated response corresponds to the conceptual representationwith the lowest ranked inquiry anomaly indicator.
 12. The method ofclaim 11, further comprising formulating responses only from theconceptual representations having the lowest ranked inquiry anomalyindicator.
 13. The method of claim 11, further comprising deriving oneor more conceptual representations until a conceptual representation isderived that has an associated inquiry anomaly indicator of the lowestrank.
 14. The method of claim 7, wherein the inquiry anomaly comprisesan inconsistency internally within the conceptual representation. 15.The method of claim 14, wherein inquiry anomalies are given a sealeddesignation relating to the magnitude of the inquiry anomaly and rankedaccording to the sealed designation.
 16. The method of claim 15, furthercomprising associating one or more inquiry anomaly indicators relatingto the rank of the inquiry anomaly with the conceptual representations.17. The method of claim 16, wherein the communicated responsecorresponds to the conceptual representation with the lowest rankedinquiry anomaly indicator.
 18. The method of claim 17, furthercomprising formulating responses only from the conceptualrepresentations having the lowest ranked inquiry anomaly indicator. 19.The method of claim 17, further comprising deriving one or moreconceptual representations until a conceptual representation is derivedthat has an associated inquiry anomaly indicator of the lowest rank. 20.The method of claim 1, wherein the step of deriving the conceptualrepresentation comprises deriving one or more response conceptualrepresentations.
 21. The method of claim 20, wherein the step offormulating one or more responses to the speech input comprisesformulation one or more responses to the speech input based on one ormore of the response conceptual representations.
 22. The method of claim1, wherein at least one of the syntactic sequences of words comprises asentence.
 23. The method of claim 1, wherein at least one of thesyntactic sequences of words comprises any syntactic organization. 24.The method of claim 1, further comprising associating semantic ruleswith each candidate word and each associated part of speech, and eachsyntactic sequence of words and each associated part of speech, whereinfurther the semantic rules relate to conceptual relationships between atleast two of the candidate words and syntactic sequences of words. 25.The method of claim 24, wherein the step of deriving the conceptualrepresentation further comprises applying the semantic rules to thesyntactic sequence of words, the candidate words or any combinationthereof.
 26. The method of claim 25, wherein the semantic rules comprisean interpreted language.
 27. The method of claim 26, wherein thesemantic rules comprise a predicate builder scripting language.
 28. Themethod of claim 27, wherein the semantic rules comprise a compiledlanguage.
 29. The method of claim 1, wherein the candidate wordscomprising the syntactic sequences of words are assigned a score orprobability, and the syntactic sequence of words is assigned a score orprobability based on the scores or probabilities of the candidate words.30. The method of claim 1, wherein each of the candidate words isconstructed based on candidate phonemes, each candidate phoneme beingassigned a score or probability, each candidate word being assigned ascore or probability based on the candidate phonemes making up thecandidate word, and the syntactic sequence of words being assigned ascore or probability based on the scores or probabilities of thecandidate words making up the syntactic sequences of words.
 31. Themethod of claim 1, wherein processing the speech input comprisesderiving candidate words from the result of the application of theHidden Markov Model (HMM) technique to the speech input, the candidatewords used to identify the syntactic sequences of words.
 32. The methodof claim 1, wherein processing the speech input comprises derivingcandidate phonemes from the result of the application of the Backus-Naur(BNF) technique to the speech input, the candidate phonemes being usedto identify the list of candidate words.
 33. The method of claim 1,wherein the step of deriving the conceptual representation comprisesapplying the principles of Conceptual Dependency to the syntacticsequences of words.
 34. The method of claim 1, wherein the step ofprocessing the speech input to identify a plurality of syntacticsequences of words comprises: inputting an acoustic input of digitizedspeech; segmenting said digitized acoustic input into plurality oftime-slices; and analyzing, each time-slice to identify one or morecandidate phoneme based on a plurality of reference cluster sets, eachcluster set representing reference phonemes for a cluster type.
 35. Themethod according to claim 1, further comprising: segmenting the speechinput into a plurality of time-slices; analyzing each time-slice toidentify one or more candidate phonemes based on a plurality ofreference cluster sets, each cluster set representing reference phonemesfor a cluster type; and defining a phoneme stream of identifiedcandidate phonemes based on the analysis, wherein at least sometime-slices are represented by alternative candidate phonemes based onsaid analyzing step. wherein the defined phoneme stream is processed toidentify at least one of the candidate words.
 36. The method of claim35, wherein the reference cluster sets are specific to at least two of a(a) region or accent, (b) a gender, and (c) an age, age range, orchild/adult distinction.
 37. The method of claim 35, wherein at leastsonic of the identified candidate phonemes for a time-slice originatefrom different cluster sets.
 38. The method of claim 35, wherein thephoneme stream comprises candidate phonemes from different cluster sets,thereby enabling recognition that the acoustic input represents speechby more than one person.
 39. The method of claim 38, wherein theprocessing step is adapted to process speech input from both male andfemale speakers.
 40. The method of claim 38, wherein the recognition isthat the speakers have different accents.
 41. The method of claim 35,wherein the segmented speech input comprises non-overlappingtime-slices.
 42. The method of claim 35, wherein the segmented speechinput comprises overlapping time-slices.
 43. The method of claim 35,wherein the segmented speech input comprises both overlapping andnonoverlapping time-slices.
 44. The method of claim 43, wherein asubsequent time-slice is selected to be overlapping or nonoverlappingbased on the results of the analysis of the previous time-slice.
 45. Themethod of claim 35, wherein the phoneme stream is further processed fortranscription or dictation.
 46. The method of claim 35, wherein thephoneme stream is further processed to provide a response to a queryrepresented by the speech input.
 47. The method of claim 46, wherein theresponse is an acoustic response.
 48. The method of claim 46, whereinthe response is a text-based response.
 49. The method of claim 46,wherein the response is a system response based on the interpretedcontent of the acoustic input.
 50. The method of claim 35, wherein theplurality of reference cluster sets correspond to more than onelanguage, thereby enabling detection of the language of the acousticinput.
 51. The method according to claim 1, further comprising:segmenting said speech input into a plurality of time-slices; analyzingeach time-slice to identify a candidate phoneme based on a plurality ofreference cluster sets, each cluster set representing reference phonemesfor that cluster type; and defining a phoneme stream of identifiedcandidate phonemes based on the analysis.
 52. The method of claim 51,wherein the reference cluster sets further include triphone variationsof the reference phonemes used in order to identify a candidate phonemebased on a triphone pronunciation.
 53. The method of claim 51, whereinthe step of analyzing comprises applying a neural network.
 54. Themethod of claim 51, wherein the step of analyzing comprises applyingformant analysis.
 55. The method of claim 51, wherein the step ofanalyzing comprises applying a multivariate Gaussian classifier.
 56. Themethod of claim 51, wherein the candidate phonemes are identified basedon application of a threshold.
 57. The method of claim 56, wherein thethreshold is fixed.
 58. The method of claim 56, wherein the threshold isadaptive.
 59. The method of claim 56, wherein there is a threshold foreach of the reference cluster sets at least some of the thresholds beingdifferent.
 60. The method of claim 56, wherein there is a threshold foreach reference phoneme for each cluster set, at least some of thereference phoneme thresholds for a given cluster set being different.61. The method of claim 56, wherein there is a threshold for eachreference phoneme for each cluster set, at least some of the clustersets having different thresholds for the same reference phoneme.
 62. Themethod of claim 51, further comprising the step of processing thephoneme stream to identify candidate words based on the candidatephonemes.
 63. The method of claim 62, wherein at least some of thecandidate words are alternative candidate words representing candidatewords from the same or an overlapping portion of the speech input. 64.The method of claim 62, wherein the candidate words are scored based onthe scores or probabilities of the candidate phonemes making up thecandidate words.
 65. The method of claim 62, wherein scoring thecandidate words comprises aggregating or averaging the scores orprobabilities of the candidate phonemes used to construct the candidatewords.
 66. The method of claim 65, further comprising ranking thecandidate words based on the scores of the candidate words.
 67. Themethod of claim 62, wherein processing the phoneme stream to identifycandidate words comprises generating search paths representing apermutation of candidate phonemes among the time-slices, each searchpath potentially representing at least a partial valid pronunciation ofa word in a dictionary.
 68. The method of claim 67, wherein a searchpath is dropped or treated as invalid when the addition of a candidatephoneme from a further time-slice would result in no at least partialvalid pronunciation of a word in a dictionary.
 69. The method of claim67, wherein a search path is dropped or treated as invalid upon theaddition of at least two non-matching candidate phonemes, a firstnon-matching candidate phoneme resulting in no correspondence to atleast a partial valid pronunciation of a word in a dictionary, and asecond non-matching candidate phoneme resulting in no correspondence toat least a partial valid pronunciation of a word in a dictionary whenignoring the first phoneme.
 70. The method of claim 62, whereinprocessing the phoneme stream to identify candidate words accounts forbridging wherein the speech comprises a phoneme which is effectivelyshared between two words.
 71. The method of claim 62, wherein processingthe phoneme stream to identify candidate words accounts for bridgingwherein the speech comprises adjacent phonemes having similarpronunciations.
 72. The method of claim 62, wherein processing thephoneme stream to identify candidate words based on the candidatephonemes is implemented, by processing candidate phonemes from atime-slice in a descending order of probability or score, therebyproviding candidate words that are naturally sorted according to adescending order of score for the candidate words.
 73. The method ofclaim 62, wherein processing the phoneme stream to identify candidatewords comprises permuting the candidate phonemes from different pointsin the acoustic input to construct combinations of phonemes comprisingpotential words.
 74. The method of claim 73, wherein the permutation isbetween different time-slices having identified candidate phonemes. 75.The method of claim 73, wherein potential words are processed accordingto a dictionary in order to identify the candidate words.
 76. The methodof claim 75, wherein the dictionary comprises a plurality of words andpronunciations of words.
 77. The method of claim 62, wherein thecandidate words correspond to at least a two-dimensional array ofcandidate word, a first dimension corresponding to time across theacoustic input, and a second dimension corresponding to alternativecandidate words for the same or an overlapping interval of time acrossthe acoustic input.
 78. The method of claim 62, wherein the candidatewords are constructed using candidate phonemes originating from the samereference cluster set.
 79. The method of claim 62, wherein the candidatewords are capable of being constructed using candidate phonemesoriginating from differing cluster sets.
 80. The method of claim 51,wherein said method is implemented in an application for transcriptionor dictation.
 81. The method of claim 51, wherein said method isimplemented in an application for generating a response to a queryrepresented by said acoustic input.
 82. The method according to claim 1,further comprising: generative a phoneme stream by processing adigitized speech sample to identify candidate phonemes including atleast some alternative candidate phonemes; and generating a list ofcandidate words for the phoneme stream based on the potential words. 83.The method of claim 82, wherein the phoneme stream is stored forperforming the permuting step at a later time.
 84. The method of claim82, where the at least one of phoneme stream and the candidate words arestored for further processing.
 85. The method of claim 82, furthercomprising processing the potential words according to a dictionary toidentify candidate words.
 86. The method of claim 85, wherein permutingthe candidate phonemes comprises permuting candidate phonemes betweendifferent time-slice to create a search path, and wherein processingaccording to a dictionary comprises processing the permuted phonemes ofthe search path to determine correspondence to at least a partial validpronunciation for a word in the dictionary.
 87. The method of claim 86,wherein a search path is expanded by permuting the search path to add acandidate phoneme from a further time-slice.
 88. The method of claim 87,wherein an expanded search path is terminated or dropped whenpermutation with the further candidate phoneme results in nocorrespondence to at least a partial valid pronunciation of a word fromthe dictionary.
 89. The method of claim 87, wherein a search path isterminated or dropped upon the permutation with at least two furtherconsecutive non-matching candidate phonemes, the first non-matchingcandidate phoneme resulting in no at least partial valid pronunciationof a word in the dictionary, and the second non-matching candidatephoneme resulting in no at least partial valid pronunciation whenignoring the first non-matching candidate phoneme, thereby providing anerror tolerant system.
 90. The method of claim 86, wherein a separatesearch path is created for each candidate phoneme in a time-slice. 91.The method of claim 90, wherein the separate search paths are created ina descending order beginning with the candidate phoneme in thetime-slice with the highest score or probability, thereby naturallysorting potential words based on scores or probabilities.
 92. The methodof claim 91, wherein further permuting a search path to add a candidatephoneme from a further time-slice comprises selecting candidate phonemesfrom the further time-slice in a descending order beginning with thecandidate phoneme with the highest score or probability.
 93. The methodof claim 82, wherein at least some of the candidate words arealternative candidate words for the same portion or an overlappingportion of the digitized speech sample.
 94. The method of claim 93,wherein the identified candidate words correspond to an at leasttwo-dimensional array of candidate words, a first dimensioncorresponding to time across the speech sample, and a second dimensioncorresponding to alternative candidate words for the same or overlappingportions of the speech sample.
 95. The method of claim 93, wherein theidentified candidate words are scored according to probabilities orscores of candidate phonemes making up the candidate words, and whereinalternative candidate words are ranked according to the scores of thealternative candidate words.
 96. The method of claim 82, whereingenerating the phoneme stream further comprises computing or identifyingscores or probabilities for the candidate phonemes.
 97. The method ofclaim 96, further comprising the step of scoring the candidate wordsbased on the scores or probabilities of the candidate phonemes making upthe candidate words.
 98. The method of claim 82, wherein the candidatephonemes are permuted by processing candidate phonemes from eachtime-slice in a descending order of probability or score, therebyproviding candidate words that are naturally sorted according to adescending order of score for the candidate words.
 99. The method ofclaim 82, wherein the phoneme stream is generated by deriving candidatewords from an N-best list of potential words generated from theapplication of the Hidden Markov Model (HMM) technique to the speechsample, further deriving additional candidate words from combinations oftwo or more consecutive N-best list potential words, and derivingcandidate phonemes from the candidate words.
 100. The method of claim82, wherein the phoneme stream is generated by deriving candidatephonemes from the results generated by application of the Backus-Naur(BNF) technique to the speech sample.
 101. The method of claim 82,further comprising the step of permuting the candidate words to generatepotential syntactic structures, the potential syntactic structurescomprising sequences of words which are potentially syntactically valid.102. The method of claim 101, further comprising the step of permutingpotential syntactic structures with at least one of(a) potentialsyntactic structures or (b) candidate words, to generate furtherpotential syntactic structures.
 103. The method of claim 101, furthercomprising syntactically analyzing the potential syntactic structures togenerate syntactically valid sequences of words.
 104. The method ofclaim 103, wherein the syntactic analysts is carried out to respectinterjections, so that the presence of interjections does not result ininvalidating an otherwise valid sequence of words.
 105. The system ofclaim 103, wherein the syntactic analysis is implemented as one of abottom-up parsing process, a top-down parsing process, an Early parsingprocess, a finite-state parsing process, and a CYK parsing process. 106.The method of claim 103, wherein syntactically analyzing comprisesapplying syntactic transform scripts to the potential syntacticstructures.
 107. The method of claim 101, further comprising identifyingat least one of the syntactically valid sequences of words as asentence, and deriving a conceptual representation of the at least onesentence.
 108. The method of claim 82, wherein the list of candidatewords is further processed for transcription or dictation.
 109. Themethod of claim 82, wherein the list of candidate words is furtherprocessed to provide a response to a query represented by the speechsample.
 110. The method of claim 82, wherein the candidate phonemes areidentified through pattern recognition applied to cluster sets ofreference phonemes.
 111. The method of claim 82, wherein the candidatephonemes are identified through pattern recognition applied to clustersets including reference triphones.
 112. The method according to claim1, further comprising: processing said speech input to identity aplurality of candidate phonemes; computing for each candidate phoneme ascore or probability; aggregating at least some of said plurality ofcandidate phonemes into potential words; and processing the computedscores or probabilities of the candidate phonemes.
 113. The method ofclaim 112, wherein the acoustic input comprises a plurality oftime-slices, the time-slices being processed to identify candidatephonemes, and wherein at least some of the time-slices are processed toidentify multiple candidate phonemes which represent alternativecandidate phonemes.
 114. The method of claim 113, wherein the identifiedcandidate phonemes are organized as a phoneme stream representing thecandidate phonemes which were capable of being detected for theplurality of times-slices.
 115. The method of claim 112, furthercomprising processing the candidate phonemes in a plurality of differentcombinations to generate a plurality of potential words, wherein atleast some of the potential words are alternative potential wordscomprising potential words for the same or an overlapping portion oftime in the speech, the potential words either comprising or beingfurther processed to define the candidate words.
 116. The method ofclaim 115, wherein processing the computed scores or probabilities ofthe candidate phonemes comprises scoring the potential words based onthe scores or probabilities of the candidate phonemes making up thepotential words.
 117. The method of claim 116, wherein the scores of thealternative potential words are used to rank the alternative potentialwords.
 118. The method of claim 116, wherein the scores of thealternative potential words are evaluated to select the alternativepotential word with the most favorable score.
 119. The method of claim112, wherein processing the computed scores or probabilities of thecandidate phonemes comprises using the computed scores or probabilitiesof the candidate phonemes to select the order in which candidatephonemes are aggregated into candidate words.
 120. The method of claim112, wherein aggregating comprises permuting at least some of thecandidate phonemes from different time-slice to generate possiblecombinations resulting in potential words.
 121. The method of claim 120,wherein processing the computed scores or probabilities of the candidatephonemes comprises using said scores or probabilities for purposes ofordering permutation of the candidate phonemes.
 122. The method of claim120, wherein potential words are processed according to a dictionary inorder to identify candidate words from the potential words.
 123. Themethod of claim 122, wherein the candidate words are identified withoutconsideration of the scores or probabilities of the candidate phonemesmaking up the potential words.
 124. The method of claim 122, wherein thecandidate words are identified by processing based on both thedictionary and the scores or probabilities of the candidate phonemesmaking up the potential words.
 125. The method of claim 112, whereinprocessing said acoustic input to identify candidate phonemes is basedon a plurality of cluster sets having reference. phonemes.
 126. Themethod of claim 112, wherein processing said acoustic input to identifycandidate phonemes is based on a plurality of cluster sets havingreference triphones.
 127. The method of claim 112, wherein the potentialwords are further processed for a transcription or dictationapplication.
 128. The method of claim 112, wherein the potential wordsare further processed for formulating a response to a query representedby the acoustic input.
 129. The method according to claim 1, wherein atleast some of the candidate words are alternative candidate wordscorresponding to the same or an overlapping portion of the speech input.130. The method of claim 129, wherein permuting at least one member ofthe list of candidate words is carried out to give consideration to wordpronunciation boundaries, thereby creating potential syntacticstructures comprised of candidate words with beginning boundaries and anend boundaries that do not conflict with the beginning boundaries andend boundaries of other candidate words pronunciations.
 131. The methodof claim 130, wherein the permutation is carried out only forcombinations of candidate words without conflicting pronunciationboundaries.
 132. The method of claim 129, further comprising permutingthe plurality of syntactic sequences with at least one of (a) potentialsyntactic structures or (b) candidate words, to generate furtherpotential syntactic sequences.
 133. The method of claim 129, furthercomprising syntactically analyzing the syntactic structures to generatesyntactically valid sequences of words.
 134. The method of claim 133,wherein the syntactic analysis is carried out to respect interjectionsso that the presence of an interjection does not invalidate an otherwisesyntactically valid sequence of words.
 135. The method of claim 133,wherein the syntactic analysis is implemented as a bottom-up parsingprocess, top-down parsing process. Early parsing process, finite-stateparsing process, or CYK parsing process.
 136. The method of claim 133,wherein syntactically analyzing comprises applying syntactic transformscripts to the syntactic structures.
 137. The method of claim 129,wherein each of the candidate words is assigned a score or probability.138. The method of claim 129, wherein each of the syntactic sequences isassigned a score or probability.
 139. The method of claim 129, whereineach of the candidate words is assigned a score or probability, andfurther wherein each of the syntactic sequences is assigned a score orprobability based on the scores or probabilities of the candidate wordsused to construct the respective syntactic sequence.
 140. The method ofclaim 129, wherein each candidate word is constructed from candidatephonemes, each candidate phoneme being assigned a score or probability,each candidate word being assigned a score or probability based on thescores or probabilities of the candidate phonemes making up thecandidate word, and further wherein each of the syntactic sequences isassigned a score or probability.
 141. The method of claim 129, whereinprocessing the speech input comprises producing the candidate wordsfront an N-best list of potential words produced by application of theHidden Markov Model (HMM) technique to the speech sample and also fromcombinations of two or more consecutive N-best list potential words.142. The method of claim 129, wherein processing the speech comprisesprocessing a series of time-slices to identify candidate phonemes, atleast some of the time segments including alternative candidatephonemes.
 143. The method of claim 129, wherein the selected syntacticsequence is a syntactically valid sequence of words comprising asentence.
 144. The method of claim 1, further comprising: segmenting thespeech input into a plurality of time-slices; analyzing each time-sliceto identify one or more candidate triphones based on a plurality ofreference cluster sets, each cluster set representing referencetriphones for a cluster type.
 145. The method of claim 144, furthercomprising processing the identified candidate triphones according to atriphone-based dictionary to identify candidate words.
 146. The methodaccording to claim 1, further comprising: communicating the syntacticsequences of words.
 147. The method of claim 146, wherein the step ofcommunicating the syntactic sequences of words comprises displaying thesyntactic sequences of words on a display.
 148. The method of claim 146,wherein the step of communicating the syntactic sequences of wordscomprises storing the syntactic sequences of words in a computer memory.149. The method of claim 146, wherein the step of communicating thesyntactic sequences of words comprises outputting the syntacticsequences of words in at least one of human readable or audible form.150. The method according to claim 1, the generating step furthercomprising: segmenting the input into a plurality of time-slices;analyzing each time-slice to identify one or more candidate wordsderived from an N-best list of potential words from an application ofthe HMM technique; and further identifying additional candidate wordsbased on combinations of two or more consecutive N-best list potentialwords.
 151. The method of claim 150, further comprising the step ofcommunicating the syntactic sequences of words by displaying thesyntactic sequences of words on a display.
 152. The method of claim 150,further comprising the step of communicating the syntactic sequences ofwords by storing the syntactic sequences of words in a computer memory.153. The method of claim 150, further comprising the step ofcommunicating the syntactic sequences of words by outputting thesyntactic sequences of words in at least one of human readable oraudible form.
 154. The method according to claim 1, said processing stepfurther comprising: segmenting the speech input into a plurality oftime-slices; and analyzing each time-slice to identify one or morecandidate words based on the application of the HMM technique.
 155. Themethod of claim 154, further comprising the step of communicating thesyntactic sequences of words by displaying the syntactic sequences ofwords on a display.
 156. The method of claim 154, further comprising thestep of communicating the syntactic sequences of words by storing thesyntactic sequences of words in a computer memory.
 157. The method ofclaim 154, further comprising the step of communicating the syntacticsequences of words by outputting the syntactic sequences of words in atleast one of human readable or audible form.
 158. A system forprocessing speech, comprising: a phoneme analyzer, receiving a speechinput, generating a list of candidate words for at least one set ofphonemes, each candidate word having a pronunciation boundary, thecandidate words being permuted to generate a plurality of potentialsyntactic structures which are valid in accordance with a set ofsyntactic rules, the candidate words and plurality of potentialsyntactic structures each having an associated part of speech; means foridentifying a plurality of syntactic sequences of words from thepotential syntactic structures and candidate words; means for derivingone or more conceptual representations from at least one of thesyntactic sequences of words; and means for formulating one or moreresponses to the speech input based on one or more of the conceptualrepresentations.
 159. The system of claim 158, wherein the means forformulating the response comprises means for processing the conceptualrepresentation in relation to reference data.
 160. The system of claim159, wherein the reference data comprises a database.
 161. The system ofclaim 159, wherein the reference data comprises a physical measurement.162. The system of claim 159, further comprising means for communicatingone or more of the responses.
 163. The system of claim 162, wherein themeans for communicating one or more of the responses comprises at leastone of audio response or visual response means.
 164. The system of claim162, wherein the means for communicating one or more of the responsescomprises text response means.
 165. The system of claim 162, wherein themeans for communicating one or more of the responses comprisesmechanical response means.
 166. The system of claim 162, furthercomprising means for identifying one or more inquiry anomalies in thespeech input for at least one of the syntactic sequences of words. 167.The system of claim 166, wherein the inquiry anomaly comprises aninconsistency between the conceptual representations and at least someof the reference data.
 168. The system of claim 167, further comprisingranking means for giving inquiry anomalies a sealed designation relatingto the magnitude of the inquiry anomaly and ranking the inquiryanomalies according to the sealed designation.
 169. The system of claim168, further comprising means to associate one or more inquiry anomalyindicators relating to the rank of the inquiry anomaly with theconceptual representations.
 170. The system of claim 169, wherein thecommunicated response corresponds to the conceptual representation withthe lowest ranked inquiry anomaly indicator.
 171. The system of claim170, further comprising means to formulate responses only fromconceptual representations having the lowest ranked inquiry anomalyindicator.
 172. The system of claim 170, further comprising means forderiving one or more conceptual representations until a conceptualrepresentation is derived that has an associated inquiry anomalyindicator of the lowest rank.
 173. The system of claim 166, wherein theinquiry anomaly comprises an inconsistency internally within theconceptual representation.
 174. The system of claim 173, furthercomprising ranking means for giving inquiry anomalies a sealeddesignation relating to the magnitude of the inquiry anomaly and rankingthe inquiry anomalies according to the sealed designation.
 175. Thesystem of claim 174, further comprising means to associate one or moreinquiry anomaly indicators relating to the rank of the inquiry anomalywith the conceptual representations.
 176. The system of claim 175,wherein the communicated response corresponds to the conceptualrepresentation with the lowest ranked inquiry anomaly indicator. 177.The system of claim 176, further comprising means to formulate responsesonly from conceptual representations having the lowest ranked inquiryanomaly indicator.
 178. The system of claim 177, further comprisingmeans for deriving one or more conceptual representation until aconceptual representation is derived that has an associated inquiryanomaly indicator of the lowest rank.
 179. The system of claim 158,further comprising means for deriving one or more responsive conceptualrepresentations.
 180. The system of claim 179, wherein the means forformulating one or more responses to the speech input comprises meansfor formulating one or more responses to the speech input based on oneor more of the responsive conceptual representations.
 181. The system ofclaim 158, wherein at least one of the syntactic sequences of wordscomprises a sentence.
 182. The system of claim 158, wherein at least oneof the syntactic sequences of words comprises any syntacticorganization.
 183. The system of claim 158, further comprising semanticrules associated with each candidate word and each associated part ofspeech, and each syntactic sequence of words and each associated part ofspeech, wherein further the semantic rules relate to conceptualrelationships between at least two of the candidate words and syntacticsequences of words.
 184. The system of claim 183, wherein the means forderiving the conceptual representation further comprises means forapplying the semantic rules to the syntactic sequence of words, thecandidate words or any combination thereof.
 185. The system of claim184, wherein the semantic rules comprise an interpreted language. 186.The system of claim 185, wherein the semantic rules comprise a predicatebuilder scripting language.
 187. The system of claim 185, wherein thesemantic rules comprise a compiled language.
 188. The system of claim158, wherein the candidate words comprising the syntactic sequences ofwords are assigned a score or probability, and the syntactic sequence ofwords is assigned a score or probability based on the scores orprobabilities of the candidate words.
 189. The system of claim 158,wherein each of the candidate words is constructed based on candidatephonemes, each candidate phoneme being assigned a score or probability,each candidate word being assigned a score or probability based on thecandidate phonemes making up the candidate word, and the syntacticsequence of words being assigned a score or probability based on thescores or probabilities of the candidate words making up the syntacticsequences of words.
 190. The system of claim 158, wherein the means foridentifying the syntactic sequences of words comprises means forderiving candidate words from the result of the application of theHidden Markov Model (HMM) technique to the speech input, the candidatewords used to identify the syntactic sequences of words.
 191. The systemof claim 158, wherein the means for processing the speech inputcomprises means for deriving candidate phonemes from the result of theapplication of the Backus-Naur (BNF) technique to the speech input, thecandidate phonemes being used to identify the list of candidate words.192. The system of claim 158, wherein the means for processing thespeech input comprises means for processing a series of time-slices toidentify candidate phonemes at least some of the time-slices includingalternative candidate phonemes, and wherein the candidate phonemes areused to identify a list of candidate words, the candidate words beingused to identity the plurality of syntactic sequences of words.
 193. Thesystem of claim 158, wherein the means for deriving the conceptualrepresentation comprises means for applying the principles of ConceptualDependency to the syntactic sequences of words.
 194. The system of claim158, wherein the means for processing the speech input to identify aplurality of syntactic sequences of words comprises; an inputting devicefor inputting an acoustic input of digitized speech; a segmented forsegmenting said digitized acoustic input into a plurality oftime-slices; and an analysis device for analyzing each time-slice toidentify one or more candidate phonemes based on a plurality ofreference cluster sets, each cluster set representing reference phonemesfor a cluster type, wherein the output of the analysis device comprisesa phoneme stream of identified candidate phonemes based on the analysis.195. The method of claim 1, wherein the step of processing at least oneof the conceptual representations comprises comparing the derivedconceptual representation to reference conceptual representations in thedatabase.
 196. The method of claim 195, wherein the step of formulatingone or more responses to the speech input comprises formulating one ormore responses to the speech input based on a successful comparison ofthe conceptual representation to at least one reference conceptualrepresentation in the database.
 197. The system of claim 158, whereinthe processing means of at least one of the conceptual representationscomprises comparing the derived conceptual representation to referenceconceptual representations in the database.
 198. The system of claim197, wherein the means for formulating one or more responses to thespeech input comprises means for formulating one or more responses tothe speech input based on a successful comparison of the conceptualrepresentations to at least one reference conceptual representation inthe database.
 199. The system according to claim 158, furthercomprising: a phoneme recognition processor for processing said speechinput based on a plurality of reference cluster sets to generate aplurality of candidate phonemes; and wherein the phoneme recognitionprocessor identifies a score or probability for each candidate phoneme.200. The system of claim 199, wherein at least some of the candidatephonemes originate from different reference cluster sets.
 201. Thesystem of claim 199, wherein the phoneme recognition processor segmentssaid speech input into time-slices in order to identify candidatephonemes.
 202. The system of claim 201, wherein the time-slices areoverlapping.
 203. The system of claim 201, wherein the time-slices arenonoverlapping.
 204. The system of claim 201, wherein the time-slicesinclude both overlapping and nonoverlapping time-slices.
 205. The systemof claim 202 or 204, wherein the overlapping time segments overlapwithin the range of approximately 40% and 60%.
 206. The system of claim204, wherein a subsequent time-slice, is selected to be overlapping ornonoverlapping based on the phoneme recognition result of the previoustime-slice.
 207. The system of claim 199, wherein the plurality ofreference cluster sets comprise sets of reference phonemes for a singlelanguage.
 208. The system of claim 199, wherein the plurality ofreference cluster sets comprise sets of reference phonemes for multiplelanguages, thereby allowing the system to detect the language spoken bythe person inputting the speech.
 209. The system of claim 199, whereinthe plurality of reference cluster sets comprise reference triphones,thereby enabling the system to recognize candidate phonemes according tothe triphone variations in the pronunciations of candidate phonemes.210. The system of claim 209, wherein the phoneme recognition processoris adapted to generate a candidate phoneme by mapping a detectedtriphone to the corresponding phoneme.
 211. The system of claim 199,wherein the phoneme recognition unit is further adapted to output aphoneme stream of the candidate phonemes comprising or associated withthe identified score or probability of each identified candidatephoneme.
 212. The system of claim 211, wherein the phoneme stream isstored for further processing.
 213. The system of claim 211, whereinsaid means for identifying a plurality of syntactic sequences of wordscomprises a phoneme stream analyzer to identify candidate wordscorresponding to the candidate phonemes.
 214. The system of claim 213,wherein the candidate words are stored for further processing.
 215. Thesystem of claim 213, wherein the phoneme stream data is stored forfurther processing.
 216. The system of claim 213, wherein the candidatewords and the phoneme stream data are stored for further processing.217. The system of claim 213, wherein the candidate words are based onpotential words constructed according to permutations of candidatephonemes from different time-slices.
 218. The system of claim 217,wherein the candidate words are generated by creating search pathsreflecting permuted candidate phonemes from different time-slicesmatching at least a partial valid pronunciation of a word in adictionary.
 219. The system of claim 218, wherein a search path isterminated or dropped upon the permutation with a further candidatephoneme resulting in no at least partial valid pronunciation of a wordin the dictionary.
 220. The system of claim 218, wherein a search pathis terminated or dropped upon the permutation with at least two furtherconsecutive non-matching candidate phonemes, the first non-matchingcandidate phoneme resulting in no at least partial valid pronunciationof a word in the dictionary, and the second non-matching candidatephoneme resulting in no at least partial valid pronunciation whenignoring the first non-matching candidate phoneme, thereby providing anerror tolerant system.
 221. The system of claim 217, wherein thecandidate words are identified by processing the potential wordsaccording to a dictionary.
 222. The system of claim 217, wherein thecandidate words are scored based on the scores or probabilities of thecandidate phonemes used to construct the candidate words.
 223. Thesystem of claim 213, wherein the candidate words are constructed basedon candidate phonemes originating from the same reference cluster set.224. The system of claim 199, wherein the phoneme recognition processoris further adapted to process said digitized acoustic input to detect orderive at least one parameter in addition to (a) candidate phonemeswhich are identified and (b) score or probabilities which areidentified, wherein the at least one additional parameter is used bysaid means for identifying a plurality of syntactic sequences inanalyzing the identified candidate phonemes to identify candidate wordscorresponding to the candidate phonemes.
 225. The system of claim 224,wherein the at least one additional parameter is derived through timedomain processing.
 226. The system of claim 224, wherein the at leastone additional parameter is derived through frequency domain processing.227. The system of claim 224, wherein the at least one additionalparameter comprises pitch information, wherein the pitch information isused in conjunction with information contained in a dictionary toidentify the candidate words.
 228. The system of claim 227, wherein thedictionary contains Chinese language words.
 229. The system of claim224, wherein the acoustic input is segmented into time-slice, eachtime-slice being characterized by a pitch value.
 230. The system ofclaim 199, wherein said system is implemented in an application fortranscription or dictation.
 231. The system of claim 199, wherein saidsystem is implemented in an application for providing a response to aquery represented by said speech input.
 232. The system according toclaim 158, further comprising: means for generating a phoneme stream byprocessing the speech input to identify candidate phonemes including atleast some alternative candidate phonemes.
 233. The system of claim 232,wherein the phoneme analyzer generates the list of candidate wordsaccording to a dictionary to identify candidate words.
 234. The systemof claim 233, wherein the candidate phonemes are permuted through asearch path created by permuting candidate phonemes from differenttime-slices and comparing the permuted candidate phonemes to thedictionary to determine if the search path corresponds to at least apartial valid pronunciation of a word.
 235. The system of claim 234,wherein the comparison is carried out based on symbols or valuesrepresenting the permuted candidate phonemes which are compared tosymbols or values in the dictionary representing partial or whole validpronunciations of a word.
 236. The system of claim 234, wherein based ona favorable result of the comparison, the search path is expanded topermute one or more candidate phonemes from additional time-slices. 237.The system of claim 236, wherein an expanded search path is terminatedwhen an additional phoneme results in the expanded search path notcorresponding to any at least partial valid pronunciation of a word inthe dictionary.
 238. The system of claim 236, wherein an expanded searchpath is terminated or dropped upon the permutation with at least twofurther consecutive non-matching candidate phonemes, the firstnon-matching candidate phoneme resulting in no at least partial validpronunciation of a word in the dictionary, and the second non-matchingcandidate phoneme resulting in no at least partial valid pronunciationwhen ignoring the first non-matching candidate phoneme, therebyproviding an error tolerant system.
 239. The system of claim 232,wherein said means for generating a phoneme stream comprises a processorexecuting the Hidden Markov Model (HMM) technique to produce candidatewords from which candidate phonemes are derived.
 240. The system ofclaim 232, wherein said means for generating a phoneme stream comprisesa processor executing the Backus-Naur (BNF) technique to produce resultsfrom which candidate phonemes are derived.
 241. The system of claim 232,wherein the phoneme stream comprises a plurality of time-slices, atleast some of the time-slices including a plurality of alternativecandidate phonemes, and each candidate phoneme having a score orprobability.
 242. The system of claim 232, wherein at least some of thecandidate words are alternative candidate words corresponding to thesame portion or an overlapping portion of the speech sample.
 243. Thesystem of claim 232, wherein the means for generating a phoneme steamprovides a score or probability for each of the candidate phonemes. 244.The system of claim 243, wherein the phoneme analyzer generates the listof candidate words based on the scores or probabilities of the candidatephonemes making up the candidate words.
 245. The system of claim 232,further comprising a memory for storing at least a two-dimensional arrayof candidate words, the first dimension related to time and the seconddimension corresponding to alternative candidate words for the same oran overlapping time period.
 246. The system of claim 232, wherein thephoneme analyzer permutes potential syntactic structures with at leastone of (i) potential syntactic structures or (ii) candidate words, togenerate further potential syntactic structures.
 247. The system ofclaim 158, wherein the syntactic analysis is carried out to respectinterjections to that the presence of an interjection does notinvalidate an otherwise syntactically valid sequence of words.
 248. Thesystem of claim 158, wherein the phoneme analyzer implements a syntacticanalysis as one of a bottom-up parsing process, a top-down parsingprocess, an Early parsing process, a finite-state parsing process, and aCYK parsing process.
 249. The system of claim 158, wherein the phonemeanalyzer applies syntactic transform scripts to the potential syntacticstructures.
 250. The system of claim 158, wherein the at least somesyntactically valid sequence of words comprise sentences.
 251. Thesystem of claim 232, wherein the means for generating a phoneme streamidentifies candidate phonemes by processing the speech sample based oncluster sets including reference phonemes.
 252. The system of claim 232,wherein the means for generating a phoneme stream identities candidatephonemes by processing the speech sample based on cluster sets includingreference triphones.
 253. The system according to claim 158, furthercomprising: phoneme recognition means for identifying a plurality ofcandidate phonemes in said speech input and providing a score ofprobability for each candidate phoneme.
 254. The speech processingsystem of claim 253, wherein the phoneme analyzer is further adapted forscoring the potential words based on the scores or probabilities of thecandidate phonemes making up the potential words.
 255. The speechprocessing system of claim 253, wherein said speech input comprises awired or wireless telephone or other wireless communication equipment.256. The speech processing system of claim 253, wherein said speechinput comprises a microphone operatively coupled to the Internet. 257.The speech processing system, of claim 253, wherein said speech inputcomprises a means for playback of pre-recorded audio.
 258. The speechprocessing system of claim 253, wherein said speech input is digitizedby a digitizer located at the speaker's location, and the digitizedspeech input is communicated to said phoneme analyzer at a differentlocation.
 259. The speech processing system of claim 258, wherein thedigitizer is located in a personal computer or personal data assistant(PDA) device.
 260. The speech processing system of claim 258, whereinthe speech input is received through a wireless transceiver, and saidwireless transceiver comprises said digitizer.
 261. The speechprocessing system of claim 253, wherein said digitizing means comprisesa digitizer remotely located from the speaker.
 262. The speechprocessing system of claim 253, wherein said phoneme recognition meansis adapted to output a phoneme stream comprising said candidate phonemesand said scores or probabilities.
 263. The speech processing system ofclaim 253, wherein the phoneme analyzer is adapted to identifyalternative potential words from the same portion or an overlappingportion of the speech input.
 264. The speech processing system of claim263, wherein the alternative potential words are examined to select thepotential word with the most favorable score based on the scores orprobabilities of the candidate phonemes making up the alternativepotential words.
 265. The speech processing system of claim 253, furthercomprising dictionary processing means for processing the potentialwords according to a dictionary to thereby identify candidate words fromthe potential words.
 266. The speech processing system of claim 253,wherein the syntactic structures are analyzed according to one of abottom-up parsing process, a top-down parsing process, an Early parsingprocess, a finite-state parsing process, and a CYK parsing process. 267.The speech processing system of claim 253, wherein said phoneme analyzeris adapted to apply syntactic transform scripts to the potentiallysyntactic structures to generate syntactically valid sequences of words.268. The speech processing system of claim 158, wherein at least some ofthe potential syntactic structures are scored based on the scores of thecandidate phonemes making up the potential syntactic structures. 269.The speech processing system of claim 268, wherein the scores of thepotential syntactic potential syntactic structures are used in selectingat least one potential syntactic structure for further analysis. 270.The speech processing system of claim 253, wherein the phonemerecognition means identifies the candidate phonemes based on referencecluster sets of reference phonemes.
 271. The speech processing system ofclaim 253 wherein, the phoneme recognition means identifies thecandidate phonemes based on reference cluster sets of referencetriphones.
 272. The system of claim 158, further comprising: a phonemerecognition unit for identifying candidate phonemes, wherein at leastsome of the candidate phonemes are alternative candidate phonemes; and aphoneme stream analyzer for identifying the candidate words constructedfrom the candidate phonemes, wherein at least some of the candidatewords are alternative candidate words corresponding to the same portionor an overlapping portion of a speech input; wherein one of theplurality of potential syntactic sequences is selected as the conceptualrepresentation corresponding to the speech input.
 273. The system ofclaim 272, wherein each of the candidate phonemes is assigned a score orprobability.
 274. The system of claim 272, wherein the word permutationunit is further adapted for syntactically validating the potentialsyntactic sequences to render syntactically valid sequences of words.275. The system of claim 274, wherein the means for deriving selectivelyextracts conceptual representation of syntactically valid sequences ofwords.
 276. The system of claim 272, wherein the phoneme stream analyzerpermutes the candidate phonemes in order to generate a list of potentialwords.
 277. The system of claim 276, wherein the list of potential wordsare selected as the list of candidate words.
 278. The system of claim276, wherein the list of potential words are processed according to adictionary to generate the list of candidate words.
 279. The system ofclaim 272, wherein the word permutation unit is further adapted forsyntactically validating the potential syntactic structures to rendervalid syntactically sequences of words, further comprising: means forcomparing the conceptual representations to reference data, said meansfor responding being sensitive to one or more successful comparisons ofthe conceptual representations in relation to the reference data.
 280. Asystem for processing speech, comprising: an input for receiving aspeech input; a processor, receiving a set of phonemes derived from aspeech input, generating a set of candidate words having respectivepronunciation boundaries from the set of phonemes, permuting thecandidate words to produce a plurality of syntactically valid potentialsyntactic structures, the candidate words and plurality of potentialsyntactic structures each having an associated part of speechidentifying a plurality of syntactic sequences of words from thepotential syntactic structures and candidate words, deriving at leastone conceptual representation from at least one of the syntacticsequences of words, and formulating at least one response to the speechinput based on one or more of the conceptual representations; and anoutput, for communicating a signal responsive to the at least oneresponse.
 281. The system according to claim 280, wherein a databasestoring reference data is provided for deriving the at least oneconceptual representation.
 282. A method for processing speech,comprising: receiving a speech input; deriving a set of phonemes fromthe speech input; generating a set of candidate words having respectivepronunciation boundaries from the set of phonemes; permuting thecandidate words to produce a plurality of syntactically valid potentialsyntactic structures, the candidate words and plurality of potentialsyntactic structures each having an associated part of speech;identifying a plurality of syntactic sequences of words from thepotential syntactic structures and candidate words; deriving at leastone conceptual representation from at least one of the syntacticsequences of words; formulating at least one response to the speechinput based on one or more of the conceptual representations; andcommunicating a signal responsive to the at least one response.
 283. Themethod according to claim 282, further comprising the step ofmaintaining a database of reference data for deriving the at least orconceptual representation.
 284. A method for processing speech,comprising; receiving an input comprising speech; identifying a list ofcandidate words constructed from a sequence of phonemes, wherein atleast some of the candidate words are alternative candidate wordscorresponding to the same portion or an overlapping portion of theinput, each respective candidate word having a pronunciation boundaryand a respective part of speech; permuting the candidate words to createa plurality of potential syntactic structures, wherein at least some ofthe plurality of potential syntactic structures is selected ascorresponding to the input and having a respective, part or parts ofspeech; syntactically validating the potential syntactic structures torender syntactically valid sequences of words; generating a plurality ofvalid syntactic sequences of words from the permuted candidate words andpotential syntactic structures; deriving conceptual representations ofsyntactically valid sequences of words; and formulating at least oneresponse to the input based on the conceptual representations.
 285. Themethod of claim 284, wherein permuting the candidate words is carriedout to give consideration to the respective pronunciation boundaries,thereby creating potential syntactic structures comprising candidatewords with pronunciation boundaries that do not conflict with theboundaries of other candidate word pronunciations.
 286. The methodaccording to claim 284, wherein the permutating the candidate words iscarried out only for combinations of candidate words without conflictingpronunciation boundaries.
 287. The method according to claim 284,further comprising permuting elements of potential syntactic structuresto generate further potential syntactic structures.
 288. The methodaccording to claim 284, further comprising syntactically analyzing thepotential syntactic structures to generate syntactically valid sequencesof words.
 289. The method according to claims 284, further comprisingthe step identifying an inquiry anomaly represented in at least one ofthe valid syntactic sequences of words, wherein the inquiry anomalycomprises an inconsistency between at least one conceptualrepresentation and at least some linguistic data determined to have ahigh probability of being represented in the communication stream. 290.A computer readable medium storing a program executable on aprogrammable computer for causing the computer to execute a method inaccordance with claim 284.